Transcutaneous analyte sensors and monitors, calibration thereof, and associated methods

ABSTRACT

Systems and methods are provided to calibrate an analyte concentration sensor within a biological system, generally using only a signal from the analyte concentration sensor. For example, at a steady state, the analyte concentration value within the biological system is known, and the same may provide a source for calibration. Similar techniques may be employed with slow-moving averages. Variations are disclosed.

INCORPORATION BY REFERENCE TO RELATED APPLICATIONS

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application is a continuation of U.S. application Ser.No. 15/261,711, filed on Sep. 9, 2016, which is a continuation, under 35U.S.C. §120, of International Patent Application No. PCT/US2016/050814,filed on Sep. 8, 2016 under the Patent Cooperation Treaty (PCT), whichdesignates the United States and claims the benefit of U.S. ProvisionalApplication No. 62/216,926, filed Sep. 10, 2015. Each of theaforementioned applications is incorporated by reference herein in itsentirety, and each is hereby expressly made a part of thisspecification.

TECHNICAL FIELD

Systems and methods for processing sensor data from continuous analytesensors and for calibration of the sensors.

BACKGROUND

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high blood sugar, which can cause anarray of physiological derangements associated with the deterioration ofsmall blood vessels, for example, kidney failure, skin ulcers, orbleeding into the vitreous of the eye. A hypoglycemic reaction (lowblood sugar) can be induced by an inadvertent overdose of insulin, orafter a normal dose of insulin or glucose-lowering agent accompanied byextraordinary exercise or insufficient food intake.

Conventionally, a person with diabetes carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a personwith diabetes normally only measures his or her glucose levels two tofour times per day. Unfortunately, such time intervals are spread so farapart that the person with diabetes likely finds out too late of ahyperglycemic or hypoglycemic condition, sometimes incurring dangerousside effects. Glucose levels may be alternatively monitored continuouslyby a sensor system including an on-skin sensor assembly. The sensorsystem may have a wireless transmitter which transmits measurement datato a receiver which can process and display information based on themeasurements.

Heretofore, a variety of glucose sensors have been developed forcontinuously measuring glucose values. Many implantable glucose sensorssuffer from complications within the body and provide only short-termand less-than-accurate sensing of blood glucose. Similarly, transdermalsensors have run into problems in accurately sensing and reporting backglucose values continuously over extended periods of time. Some effortshave been made to obtain blood glucose data from implantable devices andretrospectively determine blood glucose trends for analysis; howeverthese efforts do not aid the diabetic in determining real-time bloodglucose information. Some efforts have also been made to obtain bloodglucose data from transdermal devices for prospective data analysis,however similar problems have occurred.

In a continuous glucose monitor (CGM), after the sensor is implanted, itis calibrated, after which it provides substantially continuous sensordata to the sensor electronics. The sensor electronics convert thesensor data so that estimated analyte values can be continuouslyprovided to the user. As used herein, the terms “substantiallycontinuous,” “continuously,” etc., may refer to a data stream ofindividual measurements taken at time-spaced intervals, which may rangefrom fractions of a second up to, for example, 1, 2, or 5 minutes ormore. As the sensor electronics continue to receive sensor data, thesensor may be occasionally recalibrated to account for possible changesin sensor sensitivity and/or baseline (drift). Sensor sensitivity mayrefer to an amount of electrical current produced in the sensor by apredetermined amount of the measured analyte.

Sensor baseline refers to a signal output by the sensor when no analyteis detected. Over time, sensitivity and baseline change due to a varietyof factors, including cellular attack or migration of cells to thesensor, which can affect the ability of the analyte to reach the sensor.

This Background is provided to introduce a brief context for the Summaryand Detailed Description that follow. This Background is not intended tobe an aid in determining the scope of the claimed subject matter nor beviewed as limiting the claimed subject matter to implementations thatsolve any or all of the disadvantages or problems presented above.

SUMMARY

Without limiting the scope of the present embodiments as expressed bythe claims that follow, prominent features of systems and methodsaccording to present principles will be discussed briefly. Afterconsidering this discussion, and particularly after reading the sectionentitled “Detailed Description,” one will understand how the features ofthe present embodiments provide the advantages described herein.

In a first aspect, a method is provided of calibrating an analyteconcentration sensor within a biological system, using only a signalfrom the analyte concentration sensor, where at an occurrence of arepeatable event, the analyte concentration value within the biologicalsystem is known, including: on a monitoring device, detecting when ananalyte concentration value as measured by an analyte concentrationsensor indwelling in a biological system constitutes a first repeatableevent; and on the monitoring device or on a device or server operativelycoupled to the monitoring device, correlating a measurement of theanalyte concentration value when the biological system is at thedetected first repeatable event to the known analyte concentrationvalue.

Implementations of the embodiments and aspects may include one or moreof the following. The correlating may include determining a functionalrelationship between the sensor reading and the known analyteconcentration value. The functional relationship may include amultiplicative constant. The detecting may include waiting apredetermined time following entry of an event on the monitoring device,such as meal or exercise. The method may further include, following thecorrelating, detecting the occurrence of a second repeatable event, thesecond repeatable event different from the first repeatable event; andrecalibrating the analyte concentration sensor by correlating a sensorreading when the biological system is at the detected second repeatableevent to the known analyte concentration value. The sensor reading mayhave a first raw value at initial calibration and a second raw value atre-calibration, where the first and second raw values are different. Themethod may further include, following the correlating, displaying agraph or table indicating currently measured and historic values of theanalyte concentration as calibrated based at least in part on thecorrelating; and, following the recalibration, updating the display ofthe graph or table indicating currently measured and historic values ofthe analyte concentration according to the recalibration. The updatingmay change the display of the historic values of the analyteconcentration. The method may further include determining a differencebetween the first and second raw value; comparing a quantity based onthe difference to a predetermined criteria, and based on the comparing,determining if the sensor calibration has drifted. The method mayfurther include determining a quantitative amount that the sensorcalibration has drifted. The method may further include adjusting thesensor calibration based on the determined quantitative amount. Thequantity may be the slope between the first and second raw value. Themethod may further include, if the slope exceeds a predeterminedthreshold, prohibiting future calibrations based on steady-state untilthe slope no longer exceeds a predetermined threshold. The method mayfurther include prompting the user to enter a measured value. The sensormay be a glucose sensor. The method may further include, subsequent tothe correlating, receiving a signal from the sensor; and displaying avalue corresponding to the received signal, the displayed value based onthe received signal and the known analyte concentration value. Themethod may further include a step of determining the known analyteconcentration value by prompting the user to enter a measured value. Themethod may further include a step of determining the known analyteconcentration value by accessing a population average. Recalibrating maybe configured to occur at a time when a sensor reading is substantiallystable, or within a predetermined range of readings for threshold periodof time, whereby an occurrence of unexpected jumps in readings isreduced, and such recalibrating at such times may be caused orconfigured to occur in any of the embodiments or aspects described.

In a second aspect, a method is provided of compensating for drift in ananalyte concentration sensor within a biological system using only asignal from an analyte concentration sensor, including: measuring valuesof an analyte using an indwelling analyte concentration sensor;determining a first slow-moving average of the measured values of theanalyte over a first period of time, and basing a calibration of thesensor based at least in part on the first slow-moving average;following the first determining, determining a second slow-movingaverage of the measured values of the analyte over a second period oftime; and adjusting the calibration of the sensor based at least in parton the difference between the first slow moving average and the secondslow moving average.

Implementations of the aspects and embodiments may include one or moreof the following. The duration of the first period of time may begreater than about 12 hours or greater than about 24 hours. A durationof the first period of time may be the same as a duration of the secondperiod of time. The method may further include, following the basing thecalibration of the sensor based at least in part on the firstslow-moving average, displaying a graph or table indicating at leasthistoric values of the analyte concentration as calibrated based atleast in part on the first slow-moving average; and following theadjusting, updating the display of the graph or table indicating atleast historic values of the analyte concentration according to theadjusted calibration. The updating may change the display of thehistoric values of the analyte concentration. The displayed graph ortable may further indicate currently measured values of the analyteconcentration. The basing a calibration of the sensor based at least inpart on the first slow-moving average may further include basing thecalibration on a seed value, such as a seed value received from apopulation average or from a prior session. The method may furtherinclude, following the adjusting, changing the seed value based at leastin part on the adjusting. The method may further include changing theseed value based on the difference between the first slow-moving averageand the second slow moving average.

In a third aspect, a method is provided of compensating for drift in ananalyte concentration sensor within a biological system using only asignal from an analyte concentration sensor, including: measuring valuesof an analyte using an indwelling analyte concentration sensor;determining a first slow-moving average of the measured values of theanalyte over a first period of time, and basing a first apparentsensitivity of the sensor based at least in part on the firstslow-moving average; following the first determining, determining asecond slow-moving average of the measured values of the analyte over asecond period of time, and basing a second apparent sensitivity of thesensor based at least in part on the second slow moving average;determining if a change in apparent sensitivity of the sensor betweenthe first apparent sensitivity and the second apparent sensitivitymatches predetermined criteria; if the change in apparent sensitivitymatches predetermined criteria, then adjusting an actual sensitivity ofthe sensor to an adjusted value based on a difference between the firstand second apparent sensitivities; if the change in apparent sensitivityfails to match predetermined criteria, then prompting the user to enterdata, whereby a reason for the change in apparent sensitivity may bedetermined.

Implementations of the aspects and embodiments may include one or moreof the following. The determining may include determining if the changein apparent sensitivity is due to sensitivity drift or a change in theslow-moving average. If the change in apparent sensitivity is due to achange in the slow-moving average, then the method may further includeprompting the user to enter data pertaining to the change. Thepredetermined criteria may include known behavior for sensitivitychanges over time for a sensor. The known behavior for sensitivitychanges may constitute an envelope of acceptable sensitivity changeswith respect to time. The adjusted value may be based at least in parton the second slow moving average. The predetermined criteria mayfurther include known values for physiologically feasible analytechanges. The prompting the user may include prompting the user to entera calibration value. The prompting the user may include prompting theuser to enter meal or exercise information. Upon receiving thecalibration value or the meal or exercise information from the user, themethod may further include determining if the change in apparentsensitivity is due to sensitivity drift or a change in the slow-movingaverage. The method may further include adjusting the actual sensitivityof the sensor based on the received calibration value or meal orexercise information.

In a fourth aspect, a method is provided of checking calibration of ananalyte concentration sensor system within a biological system usingonly a signal from an analyte concentration sensor, including: after aninitial calibration, measuring values of an analyte over time using anindwelling analyte concentration sensor; calculating a clinical value ofan analyte concentration based on the measured values and the initialcalibration; adjusting the initial calibration to an updatedcalibration, the adjusting based only on the measured values of theanalyte over time or a subset thereof; calculating a clinical value ofthe analyte concentration based on a measured value and the updatedcalibration.

Implementations of the aspects and embodiments may include one or moreof the following. The initial calibration may be based on a populationaverage or data entered by a user. The adjusting may be based on aslow-moving average of the measured values of the analyte over time. Theadjusting may be based on a steady-state value of the analyte. Theinitial calibration may be based on data determined prior to a sessionassociated with the indwelling analyte concentration sensor. The datamay be determined a priori, on the bench, or in vitro.

In a fifth aspect, a method is provided of calibrating an analyteconcentration sensor within a biological system, using a signal from theanalyte concentration sensor, where at a steady state, the analyteconcentration value within the biological system is known, including: ona monitoring device, receiving a seed value of a calibration parameter;on the monitoring device, detecting when an analyte concentration valueas measured by an analyte concentration sensor indwelling in abiological system is at a steady state; and on the monitoring device oron a device or server operatively coupled to the monitoring device,correlating a measurement of the analyte concentration value when thebiological system is at the detected steady state to the known analyteconcentration value; subsequent to the correlating, receiving a signalfrom the sensor; and calculating and displaying a value corresponding tothe received signal, the calculated value based on the received signal,the known analyte concentration value, and the seed value.

Implementations of the aspects and embodiments may include one or moreof the following. The received seed value may be received from a sourceincluding factory calibration information. The method may furtherinclude detecting a behavior in the received signal outside of apre-prescribed parameter; and prompting a user to enter externalcalibration information. The displayed value may further be based on theexternal calibration information. The external calibration informationmay be received from an SMBG or a fingerstick calibration. The methodmay further include resetting the known calibration value to a new knowncalibration value, the resetting based at least partially on theexternal calibration information. The method may further includeresetting the seed value to a new seed value, the resetting based atleast partially on the external calibration information. The method mayfurther include altering the display based on a determined accuracy ofthe value. The altering the displaying may include displaying a rangerather than a value, or vice versa. The received seed value of acalibration parameter may be a user-entered characterization of diseasestate. The user entered characterization of disease state may include anindication of type I diabetes, type II diabetes, nondiabetic, orprediabetic. The received seed value of a calibration parameter may be avalue based on one or more user-entered blood glucose values. Thedisplaying of a value corresponding to the received signal may includedisplaying a graph or table indicating currently measured and historicvalues of the analyte concentration, and further including: detectingthat a change in calibration has occurred; adjusting one or morecalibration parameters of the analyte concentration sensor according tothe change in calibration; and following the adjusting, updating thedisplay of the graph or table indicating currently measured and historicvalues of the analyte concentration according to the adjustedcalibration parameters. The detecting that a change in calibration hasoccurred may include: detecting a change in a slow-moving average; ordetecting a change in the steady state value.

In a sixth aspect, a method is provided of calibrating an analyteconcentration sensor, where following sensor insertion in a patient,only parameters based on or derivable from a sensor signal are employed,including: receiving at least an initial value of an analyteconcentration and an initial value or initial distribution of values ofa sensor sensitivity; following insertion of an analyte concentrationsensor, monitoring a signal from the sensor over a duration of time;over the duration of time, calculating a plurality of analyteconcentration values based on the monitored sensor signal and theinitial value or distribution of values of the sensor sensitivity;determining a distribution of values of the monitored signal over theduration of time; optimizing the initial value or the distribution ofvalues of the sensor sensitivity and the plurality of analyteconcentration values to match the distribution of values of themonitored signal; and determining an updated sensitivity based on theoptimization.

Implementations of the aspects and embodiments may include one or moreof the following. The receiving may be of an initial distribution ofvalues of a sensor sensitivity, and the calculating a plurality ofanalyte concentration values may be based on the monitored sensor signaland a representative value from the initial distribution of values ofthe sensor sensitivity. The representative value may be chosen from anaverage or a midpoint or a median. The determining an updatedsensitivity may further include: dividing the representative value bythe initial value of the analyte concentration; and updating the valueof the sensitivity to be equal to the result of the dividing. Theinitial value of an analyte population may be a population average, maybe entered by the user, or transferred from a prior session. Theoptimizing may include optimizing a product of the initial value ordistribution of values of the sensor sensitivity and the plurality ofanalyte concentration values. The optimizing a product may includeoptimizing the product to match the distribution of values of themonitored signal while adjusting parameters of the distribution ofvalues of the sensor sensitivity and the plurality of analyteconcentration values to most closely match respective populationaverages. The receiving may further include receiving an initialdistribution of values of a baseline, and the optimizing may furtherinclude optimizing the distribution of values of the baseline along withthe distribution of values of the sensor sensitivity and the pluralityof analyte concentration values to match the distribution of values ofthe monitored signal. The initial distribution of values of a baselinemay follow a normal distribution. At least the initial value of theanalyte concentration may be used as part of a seed value input to aslow-moving average filter. The initial distribution of values of asensor sensitivity may be defined by a normal distribution. Thedetermined distribution of values of the monitored signal may follow alog normal distribution. The duration of time may be one day. The methodmay further include continuing to determine updated sensitivities basedon a prior updated sensitivity and received analyte concentrationvalues. The method may further include detecting a slow moving averageof the monitored analyte concentration values. If an absolute value of achange in the slow moving average is greater than a predeterminedthreshold over a predetermined unit of time, then the method may includeprompting a user to enter data. If an absolute value of a change in theslow moving average is greater than a predetermined threshold over apredetermined unit of time, then the method may include determining ifthe change is due a system error or due to a change in actualsensitivity of the sensor. The determining if the change is due to asystem error or due to a change in actual sensitivity of the sensor mayinclude determining if subsequent behavior of the sensitivity isconsistent with a known sensitivity profile, including with an envelopeof sensitivity curves. If the absolute value of a change in the slowmoving average is determined to be due to a change in actual sensitivityof the sensor, then the method may include updating the sensitivitybased at least in part on the value of the change in the slow movingaverage. The determining if the change is due to a system error or dueto a change in actual sensitivity of the sensor may include determiningif subsequent behavior of the analyte concentration value is consistentwith a known envelope of physiological feasibility. If the absolutevalue of a change in the slow moving average is determined to be due toa system error, then the method may include prompting the user to enterdata.

In an seventh aspect, a method is provided of calibrating an analyteconcentration sensor within a biological system, using a signal from theanalyte concentration sensor, including: receiving or determining a seedvalue of a calibration parameter relating to an analyte concentrationsensor; using the seed value to at least in part determine a calibrationof the analyte concentration sensor; and using the analyte concentrationsensor, measuring a value of an analyte concentration; and displayingthe measured value as calibrated at least in part using the seed value.

Implementations of the aspects and embodiments may include one or moreof the following. The receiving or determining may be performed on amonitoring device in operative signal communication with the analyteconcentration sensor. The displaying may be performed on the monitoringdevice or on a mobile device in signal communication with the monitoringdevice. The displaying the measured value may include displaying a graphor table indicating at least historic values of the analyteconcentration, and may further include: detecting that a change incalibration has occurred; adjusting one or more calibration parametersof the analyte concentration sensor according to the detected change incalibration; and following the adjusting, updating the display of thegraph or table indicating at least historic values of the analyteconcentration according to the adjusted calibration parameter. Theupdating may change the display of the historic values of the analyteconcentration. The seed value may be at least partially based on a code.The code may be entered by a user into a monitoring device. A monitoringdevice may be configured to receive the code without substantialinvolvement of the user. The seed value may be at least partially basedon an impedance measurement. The seed value may be at least partiallybased on information associated with a manufacturing lot of the sensor.The seed value may be at least partially based on a population average.The seed value may be at least partially based on an immediate pastanalyte value of the user.

In an eighth aspect, a method is provided of calibrating andcompensating for drift in an indwelling analyte concentration sensorwithin a biological system, using only a signal from the analyteconcentration sensor, where at a steady state, the analyte concentrationvalue within the biological system is known, including: on a monitoringdevice, detecting when an analyte concentration value as measured by ananalyte concentration sensor indwelling in a biological system is at asteady state; on the monitoring device or on a device or serveroperatively coupled to the monitoring device, correlating a measurementof the analyte concentration value when the biological system is at thedetected steady state to the known analyte concentration value;determining a first slow-moving average of the measured values of theanalyte over a first period of time, and basing a calibration of thesensor based at least in part on the first slow-moving average and onthe known analyte concentration value; following the first determining,determining a second slow-moving average of the measured values of theanalyte over a second period of time; and adjusting the calibration ofthe sensor based at least in part on the difference between the firstslow moving average and the second slow moving average.

In a ninth aspect, a method is provided of calibrating a first portionof a lot of sensors where a second portion has been subject to use,including: receiving calibration data from some of the second portion ofsensors; and updating one or more calibration parameters of the firstportion based on the received data.

Implementations of the aspects and embodiments may include one or moreof the following. The updating may be performed prior to the firstportion being installed in users. The updating may be performed afterthe first portion has been installed in users. The updating may beperformed by transmitting new or updated calibration parameters over anetwork to a monitoring device or to a sensor electronics moduleassociated with the sensor. The second portion of sensors may beconfigured to be calibrated using an a priori calibration. The secondportion of sensors may be configured to be calibrated using user data.The second portion of sensors may be configured to be calibrated usingan ex vivo bench calibration. The second portion of sensors may beconfigured to be calibrated using a blood measurement.

In a tenth aspect, a method is provided of compensating for drift in ananalyte concentration sensor within a biological system using only asignal from an analyte concentration sensor, including: measuring valuesas a function of time of an analyte using an indwelling analyteconcentration sensor; filtering the measured values using a doubleexponential smoothing filter; and following the filtering, displayingthe filtered measured values against time.

Implementations of the aspects and embodiments may include one or moreof the following. The double exponential smoothing filter may begoverned by the equations described herein. The subsequent glucosesignal as a function of time may be provided by the equations describedherein.

In an eleventh aspect, a method is provided of calibrating an analyteconcentration sensor within a biological system, using only a signalfrom the analyte concentration sensor, wherein at or during a repeatableevent, the analyte concentration value within the biological system isknown, comprising: on a monitoring device, detecting when a set ofanalyte concentration values as measured by an analyte concentrationsensor indwelling in a biological system constitutes a repeatable event;and on the monitoring device or on a device or server operativelycoupled to the monitoring device, correlating the set of analyteconcentration values at the repeatable event to the known analyteconcentration value.

Implementations may include that the repeatable event is selected fromthe group consisting of: a steady-state, a post prandial rise, a dailyhigh-low glucose spread, a decay rate, or a rate of change.

In a twelfth aspect, a method is provided of compensating for drift inan analyte concentration sensor within a biological system using only asignal from an analyte concentration sensor, comprising: measuringvalues of an analyte using an indwelling analyte concentration sensor;determining a first slow-moving average of the measured values of theanalyte over a first set of periods of time, wherein the first setincludes event-based time periods, and basing a calibration of thesensor based at least in part on the first slow-moving average;following the first determining, determining a second slow-movingaverage of the measured values of the analyte over a second set ofperiods of time, wherein the second set includes event-based timeperiods; and adjusting the calibration of the sensor based at least inpart on the difference between the first slow moving average and thesecond slow moving average.

Implementations may include one or more of the following. The first andsecond event-based time periods may be selected from the groupconsisting of: a post-prandial time period, a sleeping time period, anda post-breakfast time period.

In a thirteenth aspect, a method is provided of calibrating an analyteconcentration sensor within a biological system, comprising: for a setof sensors of a type, determining a sensitivity profile versus time; foran individual sensor of the type, measuring a sensitivity profile;measuring electrical characteristics of a transmitter; and reading anidentifier of the sensor and receiving data corresponding to sensitivityof the sensor, and storing the identifier and the received data on thetransmitter.

Implementation may include one or more of the following. The set ofsensors of a type may correspond to a set of sensors within a lot. Themethod may further include packaging the individual sensor in thetransmitter as a kit. The method may further include coupling thetransmitter to a mobile device running a monitoring application. Themethod may further include calibrating the transmitter and the sensorusing the monitoring application. The calibrating may be with respect tothe measured electrical characteristics of the transmitter. Themonitoring application may be configured to start a sensor session upona signal from a transmitter, the signal detecting that the transmitteris coupled to a sensor. The transmitter may be configured to start asensor session when the transmitter detects a coupling to a sensor. Themethod may further include coupling the transmitter to a mobile devicerunning a monitoring application. The method may further includereceiving a representative set of measured analyte values. The methodmay further include using the received representative set of measuredanalyte values, or a subset thereof, to determine a seed parameter for aforward filter, a reverse filter, or both. The seed values may bedetermined using a median signal value, a drift value, or both. Both aforward filter and a reverse filter may be employed, and the method mayfurther include optimizing the seed values to minimize a mean squarederror between the two signal filters. The method may further includeadjusting a sensitivity and a baseline for the sensor according to asignal based calibration algorithm, the signal based calibrationalgorithm using an average of the signals from the forward and reversefilters along with a raw sensor signal. The method may further includeadjusting the sensitivity and the baseline based on one or morecriteria. The criterion may include that a mean glucose value should beconsistent with an expected diabetic mean. The criterion may includethat a CGM glucose variability should be consistent with a mean glucoselevel. The method may further include detecting an amount of sensorchange, determining that the amount of sensor change is above athreshold criterion, and preventing the display of readings, wherebypotentially inaccurate readings are not displayed to a user.

In a fourteenth aspect, a method is provided of compensating for driftin an analyte concentration sensor within a biological system using onlya signal from an analyte concentration sensor, comprising: measuringvalues of an analyte using an indwelling analyte concentration sensor;determining a first slow-moving average of the measured values of theanalyte over a first period of time, and basing a calibration of thesensor based at least in part on the first slow-moving average;following the first determining, determining a second slow-movingaverage of the measured values of the analyte over a second period oftime; and adjusting the calibration of the sensor based at least in parton a seed value and on the difference between the first slow movingaverage and the second slow moving average.

Implementations may include one or more of the following. The seed valuemay be determined using a median signal value, a drift value, or both.Both a forward filter and a reverse filter may be employed, and themethod may further include optimizing seed values to minimize a meansquared error between the two signal filters. The method may furtherinclude adjusting a sensitivity and a baseline for the sensor accordingto a signal based calibration algorithm, the signal based calibrationalgorithm using an average of the signals from the forward and reversefilters along with a raw sensor signal. The method may further includeadjusting the sensitivity and the baseline based on one or morecriteria. The criterion may include that a mean glucose value should beconsistent with an expected diabetic mean. The criterion may includethat a CGM glucose variability should be consistent with a mean glucoselevel.

In further aspects and embodiments, the above method features of thevarious aspects are formulated in terms of a system as in variousaspects, configured to carry out the method features. Any of thefeatures of an embodiment of any of the aspects, including but notlimited to any embodiments of any of the first through fourteenthaspects referred to above, is applicable to all other aspects andembodiments identified herein, including but not limited to anyembodiments of any of the first through fourteenth aspects referred toabove. Moreover, any of the features of an embodiment of the variousaspects, including but not limited to any embodiments of any of thefirst through fourteenth aspects referred to above, is independentlycombinable, partly or wholly with other embodiments described herein inany way, e.g., one, two, or three or more embodiments may be combinablein whole or in part. Further, any of the features of an embodiment ofthe various aspects, including but not limited to any embodiments of anyof the first through fourteenth aspects referred to above, may be madeoptional to other aspects or embodiments. Any aspect or embodiment of amethod can be performed by a system or apparatus of another aspect orembodiment, and any aspect or embodiment of a system or apparatus can beconfigured to perform a method of another aspect or embodiment,including but not limited to any embodiments of any of the first throughfourteenth aspects referred to above.

This Summary is provided to introduce a selection of concepts in asimplified form. The concepts are further described in the DetailedDescription section. Elements or steps other than those described inthis Summary are possible, and no element or step is necessarilyrequired. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended foruse as an aid in determining the scope of the claimed subject matter.The claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present embodiments now will be discussed in detail with an emphasison highlighting the advantageous features. These embodiments depict thenovel and nonobvious sensor signal processing and calibration systemsand methods shown in the accompanying drawings, which are forillustrative purposes only and are not to scale, instead emphasizing theprinciples of the disclosure. These drawings include the followingfigures, in which like numerals indicate like parts:

FIG. 1 is a schematic view of a continuous analyte sensor systemattached to a host and communicating with a plurality of exampledevices;

FIG. 2 is a block diagram that illustrates electronics associated withthe sensor system of FIG. 1;

FIG. 3 depicts a graph illustrating a linear relationship between themeasured sensor count and analyte concentration.

FIG. 4 illustrates an exemplary change of sensitivity over time.

FIG. 5 illustrates various ways of providing factory information about asensor to transmitter electronics.

FIG. 6 illustrates “no code” options for providing factory informationabout a sensor to sensor electronics.

FIG. 7 illustrates options for providing factory information about asensor to sensor electronics without explicit user entry.

FIG. 8 illustrates options for providing factory information about asensor to sensor electronics with user entry.

FIGS. 9 and 10 illustrate steps of using one portion of a lot ofsensors, having obtained field data, to calibrate another portion of alot of sensors.

FIG. 11 is a flowchart illustrating an exemplary method according topresent principles, and in particular for performing a method accordingto FIGS. 9 and 10.

FIG. 12 is a schematic depiction of a sensor and transmitter within ahost, communicating with a receiver and/or a smart phone.

FIG. 13 is a flowchart illustrating another exemplary method accordingto present principles.

FIGS. 14 and 15 are graphs depicting an analyte concentration over timebefore (14) and after (15) a change in sensitivity.

FIG. 16 is a modular depiction of an analyte concentration measurementsystem according to present principles.

FIG. 17 is a flowchart illustrating another exemplary method accordingto present principles.

FIG. 18 is a flowchart illustrating another exemplary method accordingto present principles, employing a steady-state to perform calibration.

FIG. 19 is a graph illustrating two calibration lines, before and aftera drift has occurred.

FIGS. 20 and 21 illustrate a slow-moving average of sensor count overtime.

FIG. 22 is a flowchart illustrating another exemplary method accordingto present principles, employing a slow-moving average.

FIG. 23A is a flowchart illustrating another exemplary method accordingto present principles, illustrating updating of historical values.

FIG. 23B is a chart showing sensitivity data over an extended sensorsession, showing a characteristic drift.

FIG. 23C is a chart showing sensitivity data over an extended sensorsession, showing a characteristic drift along with a failure mode.

FIG. 24 is a flowchart illustrating another exemplary method accordingto present principles.

FIG. 25 is a flowchart illustrating another exemplary method accordingto present principles.

FIG. 26 is a flowchart illustrating another exemplary method accordingto present principles.

FIG. 27 is a flowchart illustrating another exemplary method accordingto present principles.

FIG. 28 is a graph illustrating a distribution of sensitivity.

FIG. 29 is a graph illustrating a distribution of baseline.

FIG. 30 is a graph illustrating a distribution of glucose values, e.g.,long term glucose values.

FIG. 31 is a graph illustrating a most likely sensitivity (slope) value,give exemplary parameters.

FIG. 32 is a graph illustrating a most likely baseline value, givenexemplary parameters.

FIG. 33 is a graph illustrating a most likely glucose value, givenexemplary parameters.

FIGS. 34 and 35 illustrate exemplary glucose traces. FIG. 35 alsoillustrates the effect of a double exponential filter operating on theglucose trace.

FIG. 36 illustrates an estimated drift curve for the sensor employed indetermining FIGS. 34 and 35.

FIGS. 37-39 are additional charts in which drift correction according tothe above principles is illustrated.

FIG. 40 illustrates a measured relationship between a signal coefficientof variation and a glucose concentration value standard deviation.

FIG. 41 illustrates a measured glucose concentration signal over aperiod of time.

FIG. 42 illustrates a linear relationship between signal coefficient ofvariation and glucose concentration standard deviation, with anexemplary value marked.

FIG. 43 illustrates a distribution of a difference between a measuredstandard deviation and an expected standard deviation.

FIG. 44 shows a relationship between mean glucose and glucose standarddeviation.

FIG. 45 shows distinctions between glucose standard deviations asbetween patient populations, i.e., non-diabetics, type I diabetics, andtype II diabetics.

FIG. 46 show data points separated by time lags, wherein Δ representsthe individual rate of change between two adjacent points.

FIG. 47 is a flowchart illustrating another implementation of a methodaccording to present principles.

DETAILED DESCRIPTION

The following description and examples illustrate some exampleembodiments of the disclosed invention in detail. Those of skill in theart will recognize that there are numerous variations and modificationsof this invention that are encompassed by its scope. Accordingly, thedescription of a certain example embodiment should not be deemed tolimit the scope of the present invention.

DEFINITIONS

In order to facilitate an understanding of the preferred embodiments, anumber of terms are defined below.

The term “analyte” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a substance or chemicalconstituent in a biological fluid (for example, blood, interstitialfluid, cerebral spinal fluid, lymph fluid or urine) that can beanalyzed. Analytes can include naturally occurring substances,artificial substances, metabolites, and/or reaction products. In someembodiments, the analyte for measurement by the sensor heads, devices,and methods is glucose. However, other analytes are contemplated aswell, including but not limited to acarboxyprothrombin; acylcarnitine;adenine phosphoribosyl transferase; adenosine deaminase; albumin;alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactiveprotein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-βhydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Beckermuscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobin A,hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F,D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1,Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax,sexual differentiation, 21-deoxycortisol); desbutylhalofantrine;dihydropteridine reductase; diphtheria/tetanus antitoxin; erythrocytearginase; erythrocyte protoporphyrin; esterase D; fattyacids/acylglycines; free β-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I;17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins, and hormones naturally occurring in blood or interstitialfluids can also constitute analytes in certain embodiments. The analytecan be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte can be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbiturates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body can also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA).

The terms “microprocessor” and “processor” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto a computer system, state machine, and the like that performsarithmetic and logic operations using logic circuitry that responds toand processes the basic instructions that drive a computer.

The terms “raw data stream” and “data stream” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto an analog or digital signal directly related to the measured glucosefrom the glucose sensor. In one example, the raw data stream is digitaldata in “counts” converted by an A/D converter from an analog signal(e.g., voltage or amps) and includes one or more data pointsrepresentative of a glucose concentration. The terms broadly encompass aplurality of time spaced data points from a substantially continuousglucose sensor, which comprises individual measurements taken at timeintervals ranging from fractions of a second up to, e.g., 1, 2, or 5minutes or longer. In another example, the raw data stream includes anintegrated digital value, wherein the data includes one or more datapoints representative of the glucose sensor signal averaged over a timeperiod.

The term “calibration” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the process of determining therelationship between the sensor data and the corresponding referencedata, which can be used to convert sensor data into meaningful valuessubstantially equivalent to the reference data, with or withoututilizing reference data in real time. In some embodiments, namely, incontinuous analyte sensors, calibration can be updated or recalibrated(at the factory, in real time and/or retrospectively) over time aschanges in the relationship between the sensor data and reference dataoccur, for example, due to changes in sensitivity, baseline, transport,metabolism, and the like. Calibration may also be accomplished byestimating sensor signal parameters automatically through analysis ofone or more signal characteristics or features (auto-calibration).

The terms “calibrated data” and “calibrated data stream” as used hereinare broad terms and are to be given their ordinary and customary meaningto a person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto data that has been transformed from its raw state to another stateusing a function, for example a conversion function, including by use ofa sensitivity, to provide a meaningful value to a user.

The terms “smoothed data” and “filtered data” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto data that has been modified to make it smoother and more continuousand/or to remove or diminish outlying points, for example, by performinga moving average of the raw data stream, including a slow movingaverage. Examples of data filters include FIR (finite impulse response),IIR (infinite impulse response), moving average filters, and the like.

The terms “smoothing” and “filtering” as used herein are broad terms andare to be given their ordinary and customary meaning to a person ofordinary skill in the art (and are not to be limited to a special orcustomized meaning), and furthermore refer without limitation tomodification of a set of data to make it smoother and more continuous orto remove or diminish outlying points, for example, by performing amoving average of the raw data stream.

The term “algorithm” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a computational process (forexample, programs) involved in transforming information from one stateto another, for example, by using computer processing.

The term “counts” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a unit of measurement of adigital signal. In one example, a raw data stream measured in counts isdirectly related to a voltage (e.g., converted by an A/D converter),which is directly related to current from the working electrode.

The term “sensor” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the component or region of adevice by which an analyte can be quantified.

The terms “glucose sensor” and “member for determining the amount ofglucose in a biological sample” as used herein are broad terms and areto be given their ordinary and customary meaning to a person of ordinaryskill in the art (and are not to be limited to a special or customizedmeaning), and furthermore refer without limitation to any mechanism(e.g., enzymatic or non-enzymatic) by which glucose can be quantified.For example, some embodiments utilize a membrane that contains glucoseoxidase that catalyzes the conversion of oxygen and glucose to hydrogenperoxide and gluconate, as illustrated by the following chemicalreaction:

Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule metabolized, there is a proportionalchange in the co-reactant O₂ and the product H₂O₂, one can use anelectrode to monitor the current change in either the co-reactant or theproduct to determine glucose concentration.

The terms “operably connected” and “operably linked” as used herein arebroad terms and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto one or more components being linked to another component(s) in amanner that allows transmission of signals between the components. Forexample, one or more electrodes can be used to detect the amount ofglucose in a sample and convert that information into a signal, e.g., anelectrical or electromagnetic signal; the signal can then be transmittedto an electronic circuit. In this case, the electrode is “operablylinked” to the electronic circuitry. These terms are broad enough toinclude wireless connectivity.

The term “determining” encompasses a wide variety of actions. Forexample, “determining” may include calculating, computing, processing,deriving, investigating, looking up (e.g., looking up in a table, adatabase or another data structure), ascertaining and the like. Also,“determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, calculating,deriving, establishing and/or the like. Determining may also includeascertaining that a parameter matches a predetermined criteria,including that a threshold has been met, passed, exceeded, and so on.

The term “substantially” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to being largely butnot necessarily wholly that which is specified.

The term “host” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to mammals, particularly humans.

The term “continuous analyte (or glucose) sensor” as used herein is abroad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and furthermore refers withoutlimitation to a device that continuously or continually measures aconcentration of an analyte, for example, at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes, or longer.In one exemplary embodiment, the continuous analyte sensor is a glucosesensor such as described in U.S. Pat. No. 6,001,067, which isincorporated herein by reference in its entirety.

The term “continuous analyte (or glucose) sensing” as used herein is abroad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and furthermore refers withoutlimitation to the period in which monitoring of an analyte iscontinuously or continually performed, for example, at time intervalsranging from fractions of a second up to, for example, 1, 2, or 5minutes, or longer.

The terms “reference analyte monitor,” “reference analyte meter,” and“reference analyte sensor” as used herein are broad terms and are to begiven their ordinary and customary meaning to a person of ordinary skillin the art (and are not to be limited to a special or customizedmeaning), and furthermore refer without limitation to a device thatmeasures a concentration of an analyte and can be used as a referencefor the continuous analyte sensor, for example a self-monitoring bloodglucose meter (SMBG) can be used as a reference for a continuous glucosesensor for comparison, calibration, and the like.

The term “sensing membrane” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a permeable orsemi-permeable membrane that can be comprised of two or more domains andis typically constructed of materials of a few microns thickness ormore, which are permeable to oxygen and may or may not be permeable toglucose. In one example, the sensing membrane comprises an immobilizedglucose oxidase enzyme, which enables an electrochemical reaction tooccur to measure a concentration of glucose.

The term “physiologically feasible” as used herein is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to thephysiological parameters obtained from continuous studies of glucosedata in humans and/or animals. For example, a maximal sustained rate ofchange of glucose in humans of about 4 to 5 mg/dL/min and a maximumacceleration of the rate of change of about 0.1 to 0.2 mg/dL/min/min aredeemed physiologically feasible limits. Values outside of these limitswould be considered non-physiological and likely a result of signalerror, for example. As another example, the rate of change of glucose islowest at the maxima and minima of the daily glucose range, which arethe areas of greatest risk in patient treatment, thus a physiologicallyfeasible rate of change can be set at the maxima and minima based oncontinuous studies of glucose data. As a further example, it has beenobserved that the best solution for the shape of the curve at any pointalong glucose signal data stream over a certain time period (e.g., about20 to 30 minutes) is a straight line, which can be used to setphysiological limits.

The term “frequency content” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the spectraldensity, including the frequencies contained within a signal and theirpower.

The term “linear regression” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to finding a line inwhich a set of data has a minimal measurement deviation or separationfrom that line. Byproducts of this algorithm include a slope, ay-intercept, and an R-Squared value that determine how well themeasurement data fits the line. In certain cases, robust regressiontechniques may also be employed to handle outliers in regression.

The term “non-linear regression” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to fitting a set ofdata to describe the relationship between a response variable and one ormore explanatory variables in a non-linear fashion.

The term “mean” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the sum of the observationsdivided by the number of observations.

The term “non-recursive filter” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an equation thatuses moving averages as inputs and outputs.

The terms “recursive filter” and “auto-regressive algorithm” as usedherein are broad terms and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and furthermore referwithout limitation to an equation in which previous averages are part ofthe next filtered output. More particularly, the generation of a seriesof observations whereby the value of each observation is partlydependent on the values of those that have immediately preceded it. Oneexample is a regression structure in which lagged response values assumethe role of the independent variables.

The term “variation” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a divergence or amount ofchange from a point, line, or set of data. In one embodiment, estimatedanalyte values can have a variation including a range of values outsideof the estimated analyte values that represent a range of possibilitiesbased on known physiological patterns, for example.

The terms “physiological parameters” and “physiological boundaries” asused herein are broad terms and are to be given their ordinary andcustomary meaning to a person of ordinary skill in the art (and are notto be limited to a special or customized meaning), and furthermore referwithout limitation to the parameters obtained from continuous studies ofphysiological data in humans and/or animals. For example, a maximalsustained rate of change of glucose in humans of about 6 to 8 mg/dL/minand a maximum acceleration of the rate of change of about 0.1 to 0.2mg/dL/min² are deemed physiologically feasible limits; values outside ofthese limits would be considered non-physiological. As another example,the rate of change of glucose is lowest at the maxima and minima of thedaily glucose range, which are the areas of greatest risk in patienttreatment, thus a physiologically feasible rate of change can be set atthe maxima and minima based on continuous studies of glucose data. As afurther example, it has been observed that the best solution for theshape of the curve at any point along glucose signal data stream over acertain time period (for example, about 20 to 30 minutes) is a straightline, which can be used to set physiological limits. These terms arebroad enough to include physiological parameters for any analyte.

The term “measured analyte values” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an analyte valueor set of analyte values for a time period for which analyte data hasbeen measured by an analyte sensor. The term is broad enough to includedata from the analyte sensor before or after data processing in thesensor and/or receiver (for example, data smoothing, calibration, andthe like).

The term “estimated analyte values” as used herein is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to ananalyte value or set of analyte values, which have been algorithmicallyextrapolated from measured analyte values.

The terms “sensor data,” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and are not to be limited to a special or customizedmeaning), and furthermore refers without limitation to any dataassociated with a sensor, such as a continuous analyte sensor. Sensordata includes a raw data stream, or simply data stream, of analog ordigital signals directly related to a measured analyte from an analytesensor (or other signal received from another sensor), as well ascalibrated and/or filtered raw data. In one example, the sensor datacomprises digital data in “counts” converted by an A/D converter from ananalog signal (e.g., voltage or amps) and includes one or more datapoints representative of a glucose concentration. Thus, the terms“sensor data point” and “data point” refer generally to a digitalrepresentation of sensor data at a particular time. The terms broadlyencompass a plurality of time spaced data points from a sensor, such asfrom a substantially continuous glucose sensor, which comprisesindividual measurements taken at time intervals ranging from fractionsof a second up to, e.g., 1, 2, or 5 minutes or longer. In anotherexample, the sensor data includes an integrated digital valuerepresentative of one or more data points averaged over a time period.Sensor data may include calibrated data, smoothed data, filtered data,transformed data, and/or any other data associated with a sensor.

The term “matched data pair” or “data pair” as used herein is a broadterm and is to be given its ordinary and customary meaning to a personof ordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation toreference data (for example, one or more reference analyte data points)matched with substantially time corresponding sensor data (for example,one or more sensor data points).

The term “sensor electronics,” as used herein, is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the components (for example,hardware and/or software) of a device configured to process data.

The term “calibration set” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a set of data comprisinginformation useful for calibration. In some embodiments, the calibrationset is formed from one or more matched data pairs, which are used todetermine the relationship between the reference data and the sensordata; however other data derived pre-implant, externally or internallymay also be used. As another example, data may also be employed fromprior sensor sessions of the subject user.

The terms “sensitivity” or “sensor sensitivity,” as used herein, arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and refer without limitation to anamount of signal produced by a certain concentration of a measuredanalyte, or a measured species (e.g., H₂O₂) associated with the measuredanalyte (e.g., glucose). For example, in one embodiment, a sensor has asensitivity of from about 1 to about 300 picoAmps of current for every 1mg/dL of glucose analyte.

The terms “sensitivity profile” or “sensitivity curve,” as used herein,are broad terms, and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and is not to belimited to a special or customized meaning), and refer withoutlimitation to a representation of a change in sensitivity over time.

Other definitions will be provided within the description below, and insome cases from the context of the term's usage.

As employed herein, the following abbreviations apply: Eq and Eqs(equivalents); mEq (milliequivalents); M (molar); mM (millimolar) μM(micromolar); N (Normal); mol (moles); mmol (millimoles); μmol(micromoles); nmol (nanomoles); g (grams); mg (milligrams); μg(micrograms); Kg (kilograms); L (liters); mL (milliliters); dL(deciliters); μL (microliters); cm (centimeters); mm (millimeters); μm(micrometers); nm (nanometers); h and hr (hours); min. (minutes); s andsec. (seconds); ° C. (degrees Centigrade).

Overview/General Description of System

Conventional in vivo continuous analyte sensing technology has typicallyrelied on reference measurements performed during a sensor session forcalibration of the continuous analyte sensor. The reference measurementsare matched with substantially time corresponding sensor data to creatematched data pairs. Regression is then performed on the matched datapairs (e.g., by using least squares regression) to generate a conversionfunction that defines a relationship between a sensor signal and anestimated glucose concentration.

In critical care settings, calibration of continuous analyte sensors isoften performed by using, as reference, a calibration solution with aknown concentration of the analyte. This calibration procedure can becumbersome, as a calibration bag, separate from (and an addition to) anIV (intravenous) bag, is typically used. In the ambulatory setting,calibration of continuous analyte sensors has traditionally beenperformed by capillary blood glucose measurements (e.g., a finger stickglucose test), through which reference data is obtained and entered intothe continuous analyte sensor system. This calibration proceduretypically involves frequent finger stick measurements, which can beinconvenient and painful.

Heretofore, systems and methods for in vitro calibration of a continuousanalyte sensor by the manufacturer (e.g., factory calibration), withoutreliance on periodic recalibration, have for the most part beeninadequate with respect to high levels of sensor accuracy required forglycemic management. Part of this can be attributed to changes in sensorproperties (e.g., sensor sensitivity) that can occur during sensor use.Thus, calibration of continuous analyte sensors has typically involvedperiodic inputs of reference data, whether they are associated with acalibration solution or with a finger stick measurement. This can bevery burdensome to the user during everyday life as well as to patientsin the ambulatory setting or the hospital staff in the critical caresetting.

The following description and examples described the present embodimentswith reference to the drawings. In the drawings, reference numbers labelelements of the present embodiments. These reference numbers arereproduced below in connection with the discussion of the correspondingdrawing features.

Described herein are systems and methods for calibrating continuousanalyte sensors that are capable of achieving high levels of accuracy,without (or with reduced) reliance on reference data from a referenceanalyte monitor (e.g., from a blood glucose meter).

Sensor System

FIG. 1 depicts an example system 100, in accordance with some exampleimplementations. The system 100 includes a continuous analyte sensorsystem 8 including sensor electronics 12 and a continuous analyte sensor10. The system 100 may include other devices and/or sensors, such asmedicament delivery pump 2 and glucose meter 4. The continuous analytesensor 10 may be physically connected to sensor electronics 12 and maybe integral with (e.g., non-releasably attached to) or releasablyattachable to the continuous analyte sensor 10. The sensor electronics12, medicament delivery pump 2, and/or glucose meter 4 may couple withone or more devices, such as display devices 14, 16, 18, and/or 20.

In some example implementations, the system 100 may include acloud-based analyte processor 490 configured to analyze analyte data(and/or other patient-related data) provided via network 406 (e.g., viawired, wireless, or a combination thereof) from sensor system 8 andother devices, such as display devices 14-20 and the like, associatedwith the host (also referred to as a patient) and generate reportsproviding high-level information, such as statistics, regarding themeasured analyte over a certain time frame. A full discussion of using acloud-based analyte processing system may be found in U.S. patentapplication Ser. No. 13/788,375, entitled “Cloud-Based Processing ofAnalyte Data” and filed on Mar. 7, 2013, herein incorporated byreference in its entirety.

In some example implementations, the sensor electronics 12 may includeelectronic circuitry associated with measuring and processing datagenerated by the continuous analyte sensor 10. This generated continuousanalyte sensor data may also include algorithms, which can be used toprocess and calibrate the continuous analyte sensor data, although thesealgorithms may be provided in other ways as well. The sensor electronics12 may include hardware, firmware, software, or a combination thereof,to provide measurement of levels of the analyte via a continuous analytesensor, such as a continuous glucose sensor. An example implementationof the sensor electronics 12 is described further below with respect toFIG. 2.

The sensor electronics 12 may, as noted, couple (e.g., wirelessly andthe like) with one or more devices, such as display devices 14, 16, 18,and/or 20. The display devices 14, 16, 18, and/or 20 may be configuredfor presenting information (and/or alarming), such as sensor informationtransmitted by the sensor electronics 12 for display at the displaydevices 14, 16, 18, and/or 20.

The display devices may include a relatively small, key fob-like displaydevice 14, a relatively large, hand-held display device 16, a cellularphone 18 (e.g., a smart phone, a tablet, and the like), a computer 20,and/or any other user equipment configured to at least presentinformation (e.g., medicament delivery information, discreteself-monitoring glucose readings, heart rate monitor, caloric intakemonitor, and the like).

In some example implementations, the relatively small, key fob-likedisplay device 14 may comprise a wrist watch, a belt, a necklace, apendent, a piece of jewelry, an adhesive patch, a pager, a key fob, aplastic card (e.g., credit card), an identification (ID) card, and/orthe like. This small display device 14 may include a relatively smalldisplay (e.g., smaller than the large display device 16) and may beconfigured to display certain types of displayable sensor information,such as a numerical value, and an arrow, or a color code.

In some example implementations, the relatively large, hand-held displaydevice 16 may comprise a hand-held receiver device, a palm-top computer,and/or the like. This large display device may include a relativelylarger display (e.g., larger than the small display device 14) and maybe configured to display information, such as a graphical representationof the continuous sensor data including current and historic sensor dataoutput by sensor system 8.

In some example implementations, the continuous analyte sensor 10comprises a sensor for detecting and/or measuring analytes, and thecontinuous analyte sensor 10 may be configured to continuously detectand/or measure analytes as a non-invasive device, a subcutaneous device,a transdermal device, and/or an intravascular device. In some exampleimplementations, the continuous analyte sensor 10 may analyze aplurality of intermittent blood samples, although other analytes may beused as well.

In some example implementations, the continuous analyte sensor 10 maycomprise a glucose sensor configured to measure glucose in the blood orinterstitial fluid using one or more measurement techniques, such asenzymatic, chemical, physical, electrochemical, spectrophotometric,polarimetric, calorimetric, iontophoretic, radiometric, immunochemical,and the like. In implementations in which the continuous analyte sensor10 includes a glucose sensor, the glucose sensor comprise any devicecapable of measuring the concentration of glucose and may use a varietyof techniques to measure glucose including invasive, minimally invasive,and non-invasive sensing techniques (e.g., fluorescence monitoring), toprovide data, such as a data stream, indicative of the concentration ofglucose in a host. The data stream may be sensor data (raw and/orfiltered), which may be converted into a calibrated data stream used toprovide a value of glucose to a host, such as a user, a patient, or acaretaker (e.g., a parent, a relative, a guardian, a teacher, a doctor,a nurse, or any other individual that has an interest in the wellbeingof the host). Moreover, the continuous analyte sensor 10 may beimplanted as at least one of the following types of sensors: animplantable glucose sensor, a transcutaneous glucose sensor, implantedin a host vessel or extracorporeally, a subcutaneous sensor, arefillable subcutaneous sensor, an intravascular sensor.

Although the disclosure herein refers to some implementations thatinclude a continuous analyte sensor 10 comprising a glucose sensor, thecontinuous analyte sensor 10 may comprise other types of analyte sensorsas well. Moreover, although some implementations refer to the glucosesensor as an implantable glucose sensor, other types of devices capableof detecting a concentration of glucose and providing an output signalrepresentative of glucose concentration may be used as well.Furthermore, although the description herein refers to glucose as theanalyte being measured, processed, and the like, other analytes may beused as well including, for example, ketone bodies (e.g., acetone,acetoacetic acid and beta hydroxybutyric acid, lactate, etc.), glucagon,acetyl-CoA, triglycerides, fatty acids, intermediaries in the citricacid cycle, choline, insulin, cortisol, testosterone, and the like.

FIG. 2 depicts an example of sensor electronics 12, in accordance withsome example implementations. The sensor electronics 12 may includesensor electronics that are configured to process sensor information,such as sensor data, and generate transformed sensor data anddisplayable sensor information, e.g., via a processor module. Forexample, the processor module may transform sensor data into one or moreof the following: filtered sensor data (e.g., one or more filteredanalyte concentration values), raw sensor data, calibrated sensor data(e.g., one or more calibrated analyte concentration values), rate ofchange information, trend information, rate of acceleration/decelerationinformation, sensor diagnostic information, location information,alarm/alert information, calibration information, smoothing and/orfiltering algorithms of sensor data, and/or the like.

In some embodiments, a processor module 214 is configured to achieve asubstantial portion, if not all, of the data processing. Processormodule 214 may be integral to sensor electronics 12 and/or may belocated remotely, such as in one or more of devices 14, 16, 18, and/or20 and/or cloud 490. In some embodiments, processor module 214 maycomprise a plurality of smaller subcomponents or submodules. Forexample, processor module 214 may include an alert module (not shown) orprediction module (not shown), or any other suitable module that may beutilized to efficiently process data. When processor module 214 is madeup of a plurality of submodules, the submodules may be located withinprocessor module 214, including within the sensor electronics 12 orother associated devices (e.g., 14, 16, 18, 20 and/or 490). For example,in some embodiments, processor module 214 may be located at leastpartially within a cloud-based analyte processor 490 or elsewhere innetwork 406.

In some example implementations, the processor module 214 may beconfigured to calibrate the sensor data, and the data storage memory 220may store the calibrated sensor data points as transformed sensor data.Moreover, the processor module 214 may be configured, in some exampleimplementations, to wirelessly receive calibration information from adisplay device, such as devices 14, 16, 18, and/or 20, to enablecalibration of the sensor data from sensor 12. Furthermore, theprocessor module 214 may be configured to perform additional algorithmicprocessing on the sensor data (e.g., calibrated and/or filtered dataand/or other sensor information), and the data storage memory 220 may beconfigured to store the transformed sensor data and/or sensor diagnosticinformation associated with the algorithms.

In some example implementations, the sensor electronics 12 may comprisean application-specific integrated circuit (ASIC) 205 coupled to a userinterface 222. The ASIC 205 may further include a potentiostat 210, atelemetry module 232 for transmitting data from the sensor electronics12 to one or more devices, such as devices 14, 16, 18, and/or 20, and/orother components for signal processing and data storage (e.g., processormodule 214 and data storage memory 220). Although FIG. 2 depicts ASIC205, other types of circuitry may be used as well, including fieldprogrammable gate arrays (FPGA), one or more microprocessors configuredto provide some (if not all of) the processing performed by the sensorelectronics 12, analog circuitry, digital circuitry, or a combinationthereof.

In the example depicted in FIG. 2, the potentiostat 210 is coupled to acontinuous analyte sensor 10, such as a glucose sensor to generatesensor data from the analyte. The potentiostat 210 may also provide viadata line 212 a voltage to the continuous analyte sensor 10 to bias thesensor for measurement of a value (e.g., a current and the like)indicative of the analyte concentration in a host (also referred to asthe analog portion of the sensor). The potentiostat 210 may have one ormore channels depending on the number of working electrodes at thecontinuous analyte sensor 10.

In some example implementations, the potentiostat 210 may include aresistor that translates a current value from the sensor 10 into avoltage value, while in some example implementations, acurrent-to-frequency converter (not shown) may also be configured tointegrate continuously a measured current value from the sensor 10using, for example, a charge-counting device. In some exampleimplementations, an analog-to-digital converter (not shown) may digitizethe analog signal from the sensor 10 into so-called “counts” to allowprocessing by the processor module 214. The resulting counts may bedirectly related to the current measured by the potentiostat 210, whichmay be directly related to an analyte level, such as a glucose level, inthe host.

The telemetry module 232 may be operably connected to processor module214 and may provide the hardware, firmware, and/or software that enablewireless communication between the sensor electronics 12 and one or moreother devices, such as display devices, processors, network accessdevices, and the like. A variety of wireless radio technologies that canbe implemented in the telemetry module 232 include Bluetooth, BluetoothLow-Energy, ANT, ANT+, ZigBee, IEEE 802.11, IEEE 802.16, cellular radioaccess technologies, radio frequency (RF), infrared (IR), paging networkcommunication, magnetic induction, satellite data communication, spreadspectrum communication, frequency hopping communication, near fieldcommunications, and/or the like. In some example implementations, thetelemetry module 232 comprises a Bluetooth chip, although Bluetoothtechnology may also be implemented in a combination of the telemetrymodule 232 and the processor module 214.

The processor module 214 may control the processing performed by thesensor electronics 12. For example, the processor module 214 may beconfigured to process data (e.g., counts), from the sensor, filter thedata, calibrate the data, perform fail-safe checking, and/or the like.

In some example implementations, the processor module 214 may comprise adigital filter, such as for example an infinite impulse response (IIR)or a finite impulse response (FIR) filter. This digital filter maysmooth a raw data stream received from sensor 10. Generally, digitalfilters are programmed to filter data sampled at a predetermined timeinterval (also referred to as a sample rate). In some exampleimplementations, such as when the potentiostat 210 is configured tomeasure the analyte (e.g., glucose and/or the like) at discrete timeintervals, these time intervals determine the sampling rate of thedigital filter. In some example implementations, the potentiostat 210may be configured to measure continuously the analyte, for example,using a current-to-frequency converter. In these current-to-frequencyconverter implementations, the processor module 214 may be programmed torequest, at predetermined time intervals (acquisition time), digitalvalues from the integrator of the current-to-frequency converter. Thesedigital values obtained by the processor module 214 from the integratormay be averaged over the acquisition time due to the continuity of thecurrent measurement. As such, the acquisition time may be determined bythe sampling rate of the digital filter. Other uses of FIR filters aredescribed in greater detail below.

The processor module 214 may further include a data generator (notshown) configured to generate data packages for transmission to devices,such as the display devices 14, 16, 18, and/or 20. Furthermore, theprocessor module 214 may generate data packets for transmission to theseoutside sources via telemetry module 232. In some exampleimplementations, the data packages may, as noted, be customizable foreach display device, and/or may include any available data, such as atime stamp, displayable sensor information, transformed sensor data, anidentifier code for the sensor and/or sensor electronics 12, raw data,filtered data, calibrated data, rate of change information, trendinformation, error detection or correction, and/or the like.

The processor module 214 may also include a program memory 216 and othermemory 218. The processor module 214 may be coupled to a communicationsinterface, such as a communication port 238, and a source of power, suchas a battery 234. Moreover, the battery 234 may be further coupled to abattery charger and/or regulator 236 to provide power to sensorelectronics 12 and/or charge the battery 234.

The program memory 216 may be implemented as a semi-static memory forstoring data, such as an identifier for a coupled sensor 10 (e.g., asensor identifier (ID)) and for storing code (also referred to asprogram code) to configure the ASIC 205 to perform one or more of theoperations/functions described herein. For example, the program code mayconfigure processor module 214 to process data streams or counts,filter, perform the calibration methods described below, performfail-safe checking, and the like.

The memory 218 may also be used to store information. For example, theprocessor module 214 including memory 218 may be used as the system'scache memory, where temporary storage is provided for recent sensor datareceived from the sensor. In some example implementations, the memorymay comprise memory storage components, such as read-only memory (ROM),random-access memory (RAM), dynamic-RAM, static-RAM, non-static RAM,easily erasable programmable read only memory (EEPROM), rewritable ROMs,flash memory, and the like.

The data storage memory 220 may be coupled to the processor module 214and may be configured to store a variety of sensor information. In someexample implementations, the data storage memory 220 stores one or moredays of continuous analyte sensor data. For example, the data storagememory may store 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20,and/or 30 (or more days) of continuous analyte sensor data received fromsensor 10. The stored sensor information may include one or more of thefollowing: a time stamp, raw sensor data (one or more raw analyteconcentration values), calibrated data, filtered data, transformedsensor data, and/or any other displayable sensor information,calibration information (e.g., reference BG values and/or priorcalibration information), sensor diagnostic information, and the like.

The user interface 222 may include a variety of interfaces, such as oneor more buttons 224, a liquid crystal display (LCD) 226, a vibrator 228,an audio transducer (e.g., speaker) 230, a backlight (not shown), and/orthe like. The components that comprise the user interface 222 mayprovide controls to interact with the user (e.g., the host). One or morebuttons 224 may allow, for example, toggle, menu selection, optionselection, status selection, yes/no response to on-screen questions, a“turn off” function (e.g., for an alarm), an “acknowledged” function(e.g., for an alarm), a reset, and/or the like. The LCD 226 may providethe user with, for example, visual data output. The audio transducer 230(e.g., speaker) may provide audible signals in response to triggering ofcertain alerts, such as present and/or predicted hyperglycemic andhypoglycemic conditions. In some example implementations, audiblesignals may be differentiated by tone, volume, duty cycle, pattern,duration, and/or the like. In some example implementations, the audiblesignal may be configured to be silenced (e.g., acknowledged or turnedoff) by pressing one or more buttons 224 on the sensor electronics 12and/or by signaling the sensor electronics 12 using a button orselection on a display device (e.g., key fob, cell phone, and/or thelike).

Although audio and vibratory alarms are described with respect to FIG.2, other alarming mechanisms may be used as well. For example, in someexample implementations, a tactile alarm is provided including a pokingmechanism configured to “poke” or physically contact the patient inresponse to one or more alarm conditions.

The battery 234 may be operatively connected to the processor module 214(and possibly other components of the sensor electronics 12) and providethe necessary power for the sensor electronics 12. In some exampleimplementations, the battery is a Lithium Manganese Dioxide battery,however any appropriately sized and powered battery can be used (e.g.,AAA, Nickel-cadmium, Zinc-carbon, Alkaline, Lithium, Nickel-metalhydride, Lithium-ion, Zinc-air, Zinc-mercury oxide, Silver-zinc, orhermetically-sealed). In some example implementations, the battery isrechargeable. In some example implementations, a plurality of batteriescan be used to power the system. In yet other implementations, thereceiver can be transcutaneously powered via an inductive coupling, forexample.

A battery charger and/or regulator 236 may be configured to receiveenergy from an internal and/or external charger. In some exampleimplementations, a battery regulator (or balancer) 236 regulates therecharging process by bleeding off excess charge current to allow allcells or batteries in the sensor electronics 12 to be fully chargedwithout overcharging other cells or batteries. In some exampleimplementations, the battery 234 (or batteries) is configured to becharged via an inductive and/or wireless charging pad, although anyother charging and/or power mechanism may be used as well.

One or more communication ports 238, also referred to as externalconnector(s), may be provided to allow communication with other devices,for example a PC communication (com) port can be provided to enablecommunication with systems that are separate from, or integral with, thesensor electronics 12. The communication port, for example, may comprisea serial (e.g., universal serial bus or “USB”) communication port, andallow for communicating with another computer system (e.g., PC, personaldigital assistant or “PDA,” server, or the like). In some exampleimplementations, the sensor electronics 12 is able to transmithistorical data to a PC or other computing device (e.g., an analyteprocessor as disclosed herein) for retrospective analysis by a patientand/or physician. As another example of data transmission, factoryinformation may also be sent to the algorithm from the sensor or from acloud data source.

In some continuous analyte sensor systems, an on-skin portion of thesensor electronics may be simplified to minimize complexity and/or sizeof on-skin electronics, for example, providing only raw, calibrated,and/or filtered data to a display device configured to run calibrationand other algorithms required for displaying the sensor data. However,the sensor electronics 12 (e.g., via processor module 214) may beimplemented to execute prospective algorithms used to generatetransformed sensor data and/or displayable sensor information,including, for example, algorithms that: evaluate a clinicalacceptability of reference and/or sensor data, evaluate calibration datafor best calibration based on inclusion criteria, evaluate a quality ofthe calibration, compare estimated analyte values with timecorresponding measured analyte values, analyze a variation of estimatedanalyte values, evaluate a stability of the sensor and/or sensor data,detect signal artifacts (noise), replace signal artifacts, determine arate of change and/or trend of the sensor data, perform dynamic andintelligent analyte value estimation, perform diagnostics on the sensorand/or sensor data, set modes of operation, evaluate the data foraberrancies, and/or the like.

Although separate data storage and program memories are shown in FIG. 2,a variety of configurations may be used as well. For example, one ormore memories may be used to provide storage space to support dataprocessing and storage requirements at sensor electronics 12.

Calibration

While some continuous glucose sensors rely on (and assume an accuracyof) BG values and/or factory derived information for calibration, thedisclosed embodiments exploit real-time information (e.g., including insome implementations just sensor data itself) to determine aspects ofcalibration as well as to calibrate based thereon.

In some cases calibration of an analyte sensor may use a prioricalibration distribution information. For example, in some embodiments,a priori calibration distribution information or a code can be receivedas information from a previous calibration and/or sensor session (e.g.,same sensor system, internally stored), stored in memory, coded at thefactory (e.g., as part of factory settings), on a barcode of packaging,sent from the cloud or a network of remote servers, coded by a careprovider or the user, received from another sensor system or electronicdevice, based on results from laboratory testing, and/or the like.

As used herein, a priori information includes information obtained priorto a particular calibration. For example, from previous calibrations ofa particular sensor session (e.g., feedback from a previouscalibration(s)), information obtained prior to sensor insertion (e.g.,factory information from in vitro testing or data obtained frompreviously implanted analyte concentration sensors, such as sensors ofthe same manufacturing lot of the sensor and/or sensors from one or moredifferent lots), prior in vivo testing of a similar sensor on the samehost, and/or prior in vivo testing of similar sensors on differenthosts. Calibration information includes information useful incalibrating a continuous glucose sensor, such as, but not limited to:sensitivity (m), change in sensitivity (Δdm/dt), which may also bereferred to drift in sensitivity), rate of change of sensitivity(ddm/ddt), baseline/intercept (b), change in baseline (Δdb/dt), rate ofchange of baseline (ddb/ddt), baseline and/or sensitivity profiles(i.e., change over a time period) associated with the sensor; linearity,response time, relationships between properties of the sensor (e.g.,relationships between sensitivity and baseline), or relationshipsbetween particular stimulus signal output (e.g., output indicative of animpedance, capacitance or other electrical or chemical property of thesensor) and sensor sensitivity or temperature (e.g., determined fromprior in vivo and/or ex vivo studies) such as described in U.S. PatentPublication 2012-0265035-A1, which is incorporated herein by referencein its entirety; sensor data obtained from previously implanted analyteconcentration sensors; calibration code(s) associated with a sensorbeing calibrated; patient specific relationships between sensor andsensitivity, baseline, drift, impedance, impedance/temperaturerelationship (e.g., determined from prior studies of the patient orother patients having common characteristics with the patient), site ofsensor implantation (abdomen, arm, etc.) and/or specific relationships(different sites may have different vascular density). Distributioninformation includes ranges, distribution functions, distributionparameters (mean, standard deviation, skewness, etc.), generalizedfunctions, statistical distributions, profiles, or the like thatrepresent a plurality of possible values for calibration information.Taken together, a priori calibration distribution information includesrange(s) or distribution(s) of values (e.g., describing their associatedprobabilities, probability density functions, likelihoods, or frequencyof occurrence) provided prior to a particular calibration process usefulfor calibration of the sensor (e.g., sensor data).

For example, in some embodiments, a priori calibration distributioninformation includes probability distributions for sensitivity (m) orsensitivity-related information and baseline (b) or baseline-relatedinformation based on e.g., sensor type. As described above, the priordistribution of sensitivity and/or baseline may be factory-derived(e.g., from in vitro or in vivo testing of representative sensors) orderived from previous calibrations.

As noted above, an analyte sensor generally includes an electrode tomonitor a current change in either a co-reactant or a product todetermine analyte concentration, e.g., glucose concentration. In oneexample, the sensor data comprises digital data in “counts” converted byan A/D converter from an analog signal (e.g., voltage or amps).Calibration is the process of determining the relationship between themeasured sensor signal in counts and the analyte concentration inclinical units. For example, calibration allows a given sensormeasurement in counts to be associated with a measured analyteconcentration value, e.g., in milligrams per deciliter. Referring to thegraph 10 of FIG. 3, this relationship is generally a linear one, of theform y=mx+b, where ‘y’ is the sensor signal in counts (y-axis 12), ‘x’is the clinical value of the analyte concentration (axis 14), and ‘m’ isthe sensor sensitivity, having units of [counts/(mg/dL)]. A line 16 isillustrated whose slope is termed the sensor sensitivity. ‘b’ (see linesegment 15) is the baseline sensor signal, which can be taken intoaccount, or for advanced sensors, can generally be reduced to zero ornearly zero; in any event, in many cases, the baseline can be assumed tobe small or capable of being compensated for in a predictable manner. Insome implementations a constant background signal is seen, and such aremodeled by y=m(x+c), where c is a glucose offset between the sensor siteand the blood glucose.

Once the line 16 has been determined, the system can convert a measurednumber of counts (or amps, e.g., picoamps, as described above) to aclinical value of the analyte concentration.

However, values of m and b vary from sensor to sensor and requiredetermination. In addition, the slope value m is not always constant.For example, and referring to FIG. 4, the value m can be seen to changefrom an initial sensitivity value m₀ to a final sensitivity value m_(F)over the course of time within a session. Its rate of change is seen tobe greatest in the first few days of use, and this rate of change istermed m_(R).

The slope is a function of in vivo time for a number of reasons.Particularly for initial changes in calibration, such are often due tothe sensor membrane “settling in” and achieving equilibrium with the invivo environment. Sensors are generally calibrated in vitro or on thebench, and efforts are made to make the in vitro environment as close aspossible to the in vivo one, but differences are still apparent, and thein vivo environment itself changes from user to user. In addition,sensors may vary due to differences in sterilization orshelf-life/storage conditions. Calibration changes that occur later inthe session are often due to changes in the tissue surrounding thesensor, e.g., a buildup of biofilm on the sensor.

Whatever the cause, certain effects of variability have been measuredand determined. For example, it is known that variability in the finalsensitivity m_(F) is the largest contributor to overall sensorinaccuracy. Similarly, it is known that variability in the initialsensitivity m₀ and physiology are the largest contributors to inaccuracyof sensors on the first day.

Because of the variability noted above, an initial step of calibrationincludes determining and using seed values for one or more calibrationparameters until additional data is obtained to adjust the seed value toa more accurate one.

Once calibration is achieved, the sensor and analyte concentrationmeasurement system may be employed to accurately determine clinicalvalues of an analyte concentration in a user. Such may then lead todiscrimination of other sensor behaviors, including determination oferrors and drifts in sensitivity, as are described in greater detailbelow.

The most common current method of calibration is by use of an externalblood glucose meter. Such is commonly termed a “fingerstickcalibration”, and is a well-known and common part of life for manydiabetics. This technique has the advantage of not requiring significantfactory information, and further provides a low risk of outliers. Adisadvantage is that significant user involvement is required, as wellas requiring knowledge of certain other factory calibration informationneeded for appropriate calibration. As the measurements from such metersare trusted once the meters are themselves calibrated, values from themeters can be used to calibrate an indwelling analyte concentrationmeter. Even though the sensor sensitivity changes over time as seen inFIG. 4, the changing sensitivity is immaterial if the user is willing toperform numerous external calibrations.

However, users generally do not wish to perform numerous suchcalibrations, and in many cases, e.g., patients who are type IIdiabetic, prediabetic, or even nondiabetic, the additional accuracyprovided by such calibrations is not strictly required. For example, itmay be enough for a user to know what range they are in, rather than anexact analyte concentration value. In another implementation, data maybe provided with an associated confidence interval, to let the user knowhow much confidence to place in the displayed data.

Thus efforts have been made to reduce the number of calibrations.Nevertheless, many current CGM systems still require a blood glucosevalue to be used for at least an initial calibration and the same isalso often required when dosing. Present systems and methods accordingto present principles are directed in part to ways of reducing oreliminating such required calibrations.

One simple and convenient way of providing some level of calibration isby use of calibration information about related sensors. Even if thecalibration information is approximate, such may still be sufficient foruse by certain groups of patients. For example, and referring to theflowchart 18 of FIG. 5, if factory calibration is known about one ormore sensors within a manufacturing lot, then this information may beprovided to other sensors in the manufacturing lot that have not yetbeen used in patients (step 20). This step is often termed providing a“code” to a transmitter, as codes are often used to identify themanufacturing lot (and thus details) of the sensor as the same arecoupled together during insertion in a patient as part of a CGM system.The transmitter may then identify the manufacturing lot from the code,and apply appropriate calibration parameters according to a lookup tableor other technique. However, it should be understood that the code maybe provided not just to a transmitter but to any device in which countsmay be converted to clinical units, e.g., a dedicated receiver, anoff-the-shelf device which may be employed to receive and displayanalyte concentrations, e.g., a smart phone, tablet computer, or thelike. In addition, the code may not be a code in a typical sense but maysimply provide any identifier to any device requiring the same forcalibration purposes.

Referring again to FIG. 5, ways are described of accomplishing the stepof providing factory information about the sensor to the transmitter orother electronics (step 20). Some of these ways constitute options forproviding calibration information without using codes (step 22). Othersinclude ways of providing a code using a step of user data entry (step28). In some cases, codes may be entered without user entry (step 24).Still other ways of providing codes may be used (step 26), and suchadditional ways are also described below. Details of these methods arenow described.

Referring to the flowchart 30 of FIG. 6, examples of providing factorycalibration information in the absence of the code (step 22) aredescribed. A first way is using a representative value, e.g., average ormedian or other measure, e.g., range, of the manufacturing lot (step34). That is, if an average is known of the manufacturing lot, or evenif an average is known of a different manufacturing lot, manufacturedusing the same techniques, then it may be assumed that the sensorcalibration parameters will be similar and thus may be used as part of acalibration of a new sensor. Alternatively, a predictable relationshipmay also be employed to interpolate sensor calibration parameters, e.g.,bracketing sensor lots.

As another example, impedance measurements may be employed in thedetermination of calibration parameters (step 40).

Calibrations may also occur using information about prior calibrations(step 38). For example, if a user just switched out a sensor which wascalibrated and measuring the user's glucose concentration at, e.g., 120mg/dL, then it may be assumed that a proper measurement of a newlyinserted sensor should be such that the user's glucose value is again120 mg/dL. In some cases, if a predicted glucose value has beendetermined, the predicted value may be used for the newly insertedsensor. Even if a short period of time has elapsed between the lastreading of the old sensor and a new sensor reading, recognition ofphysiologically feasible glucose changes will lead to bounds in what thenew measurement can be, and thus what the calibration parameters of thenew sensor can be.

In yet another variation, various self-calibrating algorithms may beemployed (step 36) in order to self-calibrate a CGM system. In thissense CGM systems may be termed as becoming “self-aware”. For example, aCGM system may be seeded with an average glucose value if known, e.g.,from a prior session, including use of a prior session steady statevalue or a prior session slow moving average, as will be described ingreater detail below. CGM systems may also be seeded with A1C values ifavailable. Various assumptions may also be made if appropriate. Theseeded average value can be represented by a distribution, techniquesfor which will also be described in greater detail below.

FIG. 7 describes systems in which codes may be provided from a sensor toa transmitter without a step of user entry (step 24). Again it is notedthat while language is used here about providing the code to atransmitter, it will be understood that codes may be provided to variousdevices in signal communication with the transmitter as well, includinga dedicated receiver, smart phone, tablet computer, follower device, orother computing environment.

In the implementations of the diagram 42 of FIG. 7, codes or the likeare provided to the transmitter but without significant userinvolvement. For example, a degree of encoding may be accomplished bysending a manufacturing lot of sensors to different markets (step 46).In one implementation, sensors with like codes may be sent to differentgeographic locations (step 48). For example, sensors sent to aparticular geographic region may be from a similar or the samemanufacturing lot, and when the same is inserted and makes initialcontact with the network, calibration parameters known for that lot maybe provided to the transmitter, thus providing an immediate degree ofcalibration based on geographic location. Geolocation may be employed toidentify a location, and the location may then be used to identify orcategorize a sensor.

In the same way, sensors of like lots may be grouped by product, sodifferent codes then are associated with different products (step 54).For example, a first product may have a first code associated with it,and all the sensors for that product may be manufactured in the same ora similar way, leading to little manufacturing variability betweensensors associated with the particular product. In this case, once theproduct is identified, the associated sensor calibration parameters maybe uniquely determined, at least as an average.

In another implementation, without specific regard to geographiclocation or product, a certain group of sensors having a particular codemay be shipped with a code particularly associated with the respectiveuser's transmitter (step 56). In this case once the calibrationparameters are known for one member of the group sent, and such may beknown long before the group is sent, then the calibration parameters forthe rest of the group are also known.

RFID technology may also be used to identify a manufacturing lot ofsensors (step 58). For example, a small RFID chip may be located on thebase of the sensor, and may be read by the transmitter when the sensorand transmitter are coupled (step 60). In another implementation, anRFID may be read by the receiver (step 62), or alternatively a smartphone or other device. Alternatively, the RFID device may be located onthe applicator, and the transmitter may again read the identificationinformation (and thus calibration information) when the applicator isused to install a sensor in a patient.

In yet another implementation, near field communications (NFC) may beemployed on the packaging or on any other component of the system (step66) to communicate the identification information.

Other types of communication schemes may be employed to communicate theinformation from a sensor to a transmitter. For example, a mechanicalsensor on the transmitter may allow communication of code information(step 72), e.g., bumps, vertical pins, a mechanical system sensingorientation of the transmitter to the base, or other mechanical elementsreadable by the transmitter. Magnetic sensors may be employed for thesame purpose (step 78), and in the same way an optical reader on thetransmitter may be employed (step 74) to read, e.g., barcodes or QRcodes, as well as other identifying marks or colors. Resistive sensorsmay also be employed (step 76), or other sensors detecting a state ofconnection. For example, sensors of different codes may be provided withrespective different lengths. Sensors may be provided with multiplecontact pads that the sensors are aligned to. Sensors of different codesmay be provided with different resistances, and measurement of the samemay determine the code.

FIG. 8 illustrates a diagram 80 showing ways of code communicationemploying user data entry (step 28). In perhaps the simplest, the codemay be provided to the user upon purchase, and the same simply manuallyentered (step 84) into the receiver, smart phone, or other device with aUI allowing data entry. For example, the user may input text, a number,a color, or the like. The sensor may also ship with a card, e.g., a SIMcard, and the SIM card may be inserted into the receiver (step 90) toallow calibration information to be communicated but without requiringthat the user enter a manual code. The transmitter may be provided witha switch system (step 88), and the user may adjust the position ofswitches on the transmitter according to instructions on the receivedsensor. For example, the transmitter switch may be a four positionswitch, or a DIP switch, and by appropriate adjustment, the user mayprovide a code associated with the sensor to the transmitter. Thereceiver or smart phone may also be enabled to scan a label associatedwith the sensor, via an integral camera or barcode reader, to allow theinformation to be communicated in that fashion. The scan may be of a barlabel, QR code, or the like.

One variation is described below with respect to FIGS. 9-11. Thisimplementation employs human data from the field to improve or enable afactory calibration. In more detail, factory calibration parameters(e.g., sensitivity and baseline over time) are often best identifiedusing human data. Although bench data correlates with human data, thecorrelation is not yet perfect and there are often offsets in thecorrelations. Having access to human data generated by each lot ofsensors produced during manufacturing would generally be the best dataset to generate factory calibration information. Factory calibrationparameters can change between lots and so characterizing each lot may beadvantageous as improvements are made.

FIGS. 9-11 shows a method that uses data collected in humans using partof a lot of sensors to generate or adjust the factory calibrationnumbers for the rest of the lot. There are several arrangements of thismethod.

In one arrangement, calibrated sensors are sent to the market for use bypatients. As such sensors are calibrated in connected systems, e.g., viaa blood glucose calibration techniques or other calibration techniques,including those using only the CGM signal itself, calibrationinformation may be returned to the manufacturer through the cloud orother Internet-based network. The information may be used to generatethe factory calibration settings for the remainder of the manufacturinglot of sensors that were not sent out to the market, and the same maythen be shipped.

In another implementation, there is an initial factory calibrationsetting that is shipped with the product. Again cloud or networkinformation may be monitored, and a determination may be made as to howclosely the actual parameters match the initial factory calibrationsettings. Adjustments may then be made to the factory calibrationsetting of the unshipped sensors based on this determined closeness. Inthis implementation and in the prior one, release of the sensor productsmay be staggered so that subsequent shipments have improved accuracy.This implementation may further be performed even if all of the sensorshave been shipped, as adjustments may be performed through the networkor through the cloud.

In more detail, and referring to FIG. 9, a factory 148 is illustratedhaving a manufacturing lot or batch of sensors 150, the manufacturinglot or batch generally created in the same (or a very similar)manufacturing process. The lot or batch 150 may be divided into a firstportion 154 and a second portion 156. The first portion 154 maytemporarily stay with the factory 148, while the second portion 156 maybe sent to a group of users 158.

Referring next to FIG. 10, data from the second portion 156 may be usedat the factory 148 to inform the factory calibration of the firstportion 154, transforming the same into a calibrated first portion 154′.If the first portion 154 has already been shipped, then within theuser's group 158 the first portion may be calibrated before or afterinsertion, indicated as first portion 154″. Calibration of the firstportion following shipment may occur as noted above, by access to anetwork or cloud resource about factory calibration information,particularly where the same has been updated with data from sensors inthe field.

FIG. 11 is a flowchart 160 illustrating the above-described method.First, a manufacturing lot or batch of sensors is manufactured underknown and reproducible conditions at a factory (step 162). The lot orbatch is divided into at least two portions (step 164). Two portions aredescribed here for convenience, but it will be understood that themanufacturing lot may be divided for staggered release into any numberof portions.

In this example, the second portion is sent to users (step 166). Thesecond portion is then calibrated (step 168), and the calibration mayoccur in known fashion, e.g., using a priori information, benchcalibration values, user data, finger stick calibrations, or the like.The calibration may also occur using techniques disclosed here.

The calibration information from the second portion may then be sent tothe factory (step 170). The calibration of the first portion of thesensors may then be generated or adjusted based on data from the secondportion (step 172). That is, if a factory calibration has been generatedfor the first portion, the same may be adjusted if required. If nofactory calibration has been generated, received data from the secondportion may be used to inform the calibration of the first portion,e.g., an average of determined sensitivities from the field, and so on.The adjustment or generation may occur at the factory (step 174), or thesame may occur following shipment, before or after insertion in apatient (step 176), where the transmitter, receiver, or other monitoringdevice, e.g., smart phone, are in network communication with a server orother network resource operated by the factory 148.

Once sensors are inserted in a user and initial calibration is complete,any calibration from then on is termed “ongoing” or “continuing”calibration. Schematically this is illustrated in the diagram 102 ofFIG. 12. A user, patient, or host 112 has an indwelling sensor 114,which is connected to a transmitter 115. In many cases the transmitteris used multiple times for different sensors. In other cases, thetransmitter may be made disposable.

The transmitter 115 allows communication of signals measured by thesensor 114 to devices such as a receiver or dedicated device 104, or asmart phone 108. The receiver 104 is shown with a display 106, and thesmart phone 108 is shown with a display 110. The displays 106 or 110 maybe employed to indicate to the user clinical values of analyteconcentrations, e.g., glucose concentrations. In doing so they rely uponthe relationship noted above, that a measured current or counts isrelated to a clinical value of analyte concentration by a linearrelationship having a slope representing the sensitivity.

Systems and methods according to present principles describe thedevelopment or determination of this linear relationship based largelyor solely on characteristics of the sensor signal itself and do notrely, in some implementations, on external data, as prior systems did.In addition, such “self-aware” systems, employing “self” or “auto”calibration, may be employed not only to more accurately measuresubsequent analyte concentrations but also to retrospectively modifyresults of prior measurements. In this way, when such is displayed on adisplay such as, e.g., display 106 or display 110, the same moreaccurately conveys measured data. Put another way, retrospectiveprocessing may be employed to correct or modify prior calibrations, andeven to update data measured therefrom. In this way, if a displayindicates historical data as well as current data, at least thehistorical data will be updated, i.e., the display of such will change,to reflect calibration parameters that are better known or known withmore confidence than prior calibrations.

This method is illustrated by the flowchart 116 of FIG. 13, in which afirst step is the reception or determination of a seed value (step 118),such as may be received or determined using the initial calibrationprocedures described above. The seed value may then be employed todetermine the calibration (step 120). For example, if the receivedcalibration parameter is a particular value of the slope or sensitivitym, then the same may be used to relate counts measured to a clinicalvalue of analyte concentration, and may be used to begin immediatelynotifying the user of their measured analyte concentration, e.g.,glucose measurement. That is, the analyte may be measured with thesensor (step 122), and the measured value may be displayed to the userbased at least in part on the seed value received in step 118 (step124).

In some cases a change in calibration will occur (step 126), and thesame may be detected in various ways, including ways described below.The calibration, and in particular the calibration parameters includingsensitivity and baseline, may then be adjusted (step 128). Upon theupdating of the calibration parameters, the display may be updated (step130).

As noted above, the updating of the display may not only refer toadjusting a currently measured value of analyte concentration, but alsorecalculating and thus changing the display of historic values based onthe adjusted calibration. For example, a sensitivity may have been“seeded” by an initial value but following the receipt of data may bedetermined to actually be lower than the initial seed value by 10%. Inthis case, it is not just ongoing displayed analyte values that will beadjusted but also, in one implementation, historic values may beadjusted to reflect the updated sensitivity. This example illustrates asituation where a seed value is updated with the measured value. In somecases, a previously determined value (determined by seed or measurement)may be updated with a later determined value. This situation may arise,e.g., when a sensor calibration parameter “drifts”. For example, if thecalibration of the sensor is determined to have drifted, a change may bemade to the calibration parameters such that the receiver, smart phone,or other monitoring device continues to display an accurate value of theanalyte concentration. In one implementation, if it can be determinedwhen the drift occurred, certain historic values may be updated in adisplay, i.e., those measured following the drift, while others need notbe updated, e.g., those measured prior to the drift.

In one implementation, if the determined seed value and the initial seedvalue are close, e.g., within 10%, then the initial seed value (or othercalibration parameter) may simply be adjusted accordingly. However, ifthe value is further away, then user may be prompted for intervention,e.g., by an optional finger stick.

FIGS. 14 and 15 are graphs depicting an analyte concentration over timebefore (14) and after (15) a change in sensitivity. In particular, FIG.14 illustrates a graph 132 in which a plot 138 is shown of an analyteconcentration over time. The axis 134 represents values of the analyteconcentration and the axis 136 represents time. Following a change insensitivity, the graph becomes graph 140, with transformed historicanalyte values 146. The change in sensitivity may be considered as anupdated sensitivity or as an updated seed value, depending onimplementation. Multiple other ways of adjusting calibration usingsensor signal characteristics may be employed including, but not limitedto, mean sensor signal, standard deviation or CV (coefficient ofvariation) or inter quartile range of sensor signal or other higherorder or rank-order statistics.

In more detail, and in contrast to prior efforts, the preferredembodiments describe systems and methods for periodically orsubstantially continuously post-processing (e.g., updating) thesubstantially real-time graphical representation of glucose data (e.g.,trend graph representative of glucose concentration over a previousnumber of minutes or hours) with processed data, where the data has beenprocessed responsive to updates in calibration, e.g., as a result ofsensor drift, system errors, or the like.

Referring to the analyte concentration measurement system 135 depictedin FIG. 16, and in particular at block 137, a sensor data receivingmodule, also referred to as the sensor data module, or processor module,receives sensor data (e.g., a data stream), including one or moretime-spaced sensor data points. In some embodiments, the data stream isstored in the sensor for additional processing; in some alternativeembodiments, the sensor periodically transmits the data stream to thereceiver or other monitoring device, such as a smart phone, which can bein wired or wireless communication with the sensor. In some embodiments,raw and/or filtered data is stored in the sensor and/or transmitted andstored in the receiver.

At block 139, the processor module is configured to process the sensordata in various ways. The processor module, in combination with acalibration module 143, may also be employed to determine whether achange in calibration has occurred, such as described in more detailabove and below. In more detail, at block 143, a calibration moduledetects changes in calibration and more particularly changes in thesensitivity using data in the data stream.

At block 141, an output module provides output to the user via a userinterface (not shown). The output is representative of the estimatedglucose value, which is determined by converting the sensor data into ameaningful clinical glucose value. User output can be in the form of anumeric estimated glucose value, an indication of a directional trend ofglucose concentration, and/or a graphical representation of theestimated glucose data over a period of time, for example. Otherrepresentations of the estimated glucose values are also possible, forexample audio and tactile. In some embodiments, the output moduledisplays both a “real-time” glucose value (e.g., a number representativeof the most recently measure glucose concentration) and a graphicalrepresentation of the processed and/or postprocessed sensor data.

In one embodiment, the estimated glucose value is represented by anumeric value. In other exemplary embodiments, the user interfacegraphically represents the estimated glucose data trend over apredetermined time period (e.g., one, three, and nine hours,respectively). In alternative embodiments, other time periods can berepresented. In alternative embodiments, pictures, animation, charts,graphs, ranges of values, and numeric data can be selectively displayed.

The processor module may further be configured to perform a step ofpostprocessing, e.g., may be configured to periodically or substantiallycontinuously post-process (e.g., update) the displayed graphicalrepresentation of the data corresponding to the time period according tothe received data, e.g., more recently received data. For example, theglucose trend information (e.g., for, the previous 1-, 3-, or 9-hourtrend graphs) can be updated to better represent actual glucose valuesconsidering newly-determined calibration values. In some embodiments,the post processing module post-processes segments of data (e.g., 1-,3-, or 9-hour trend graph data) every few seconds, minutes, hours, days,or anywhere in between, and/or when requested by a user (e.g., inresponsive to a button-activation such as a request for display of a3-hour trend graph).

In general, post-processing includes the processing performed by theprocessor module (e.g., within the hand-held receiver unit) on “recent”sensor data (e.g., data that is inclusive of time points within the pastfew minutes to few hours) after its initial display of the sensor dataand prior to what is generally termed “retrospective analysis” in theart (e.g., analysis that is typically accomplished retrospectively atone time, in contrast to intermittently, periodically, or continuously,on an entire data set, such as for display of sensor data for physiciananalysis). Post-processing can include programming performed torecalibrate the sensor data (e.g., to better match to reference values),fill in data gaps (e.g., data eliminated due to noise or otherproblems), smooth out (filter) sensor data, compensate for a time lag inthe sensor data, and the like. Preferably, the post-processed data isdisplayed on a personal hand-held unit (e.g., such as on the 1-, 3-, and9-hour trend graphs of the receiver or smart phone) in “real time”(e.g., inclusive of recent data within the past few minutes or hours)and can be updated (post-processed) automatically (e.g., periodically,intermittently, or continuously) or selectively (e.g., responsive to arequest) when new or additional information is available (e.g., newreference data, new sensor data, etc.). In some alternative embodiments,post-processing can be triggered dependent upon the duration of a changein calibration episode; for example, data associated with changes incalibration events extending past about 30 minutes can be processedand/or displayed differently than data during the initial 30 minutes ofa change in calibration episode.

In one exemplary embodiment, the processor module filters the datastream to recalculate data for a previous time period and periodicallyor substantially continuously displays a graphical representation of therecalculated data for that time period (e.g., trend graph). In anotherexemplary embodiment, the processor module adjusts the data for a timelag (e.g., removes a time lag induced by real-time filtering) from datafor a previous time period and displays a graphical representation ofthe time lag adjusted data for that time period (e.g., trend graph). Inanother exemplary embodiment, the processor module algorithmicallysmoothes one or more sensor data points over a moving window (e.g.,including time points before and after the one or more sensor datapoints) for data for a previous time period and displays a graphicalrepresentation of the updated, averaged, or smoothed data for that timeperiod (e.g., trend graph).

In some embodiments, the processor module is configured to filter thesensor data and to display a graphical representation of the filteredsensor data responsive to a determination of a start of a change incalibration event. In some embodiments, the processor module isconfigured to display a graphical representation of unfiltered data(e.g., raw data) responsive to a determination of an end of a change incalibration event. In some embodiments, the processor module isconfigured to display a graphical representation of unfiltered dataexcept when a change in calibration event is determined. It has beenfound that adaptive filtering as described herein, including selectivefiltering during a change in calibration events, increases accuracy ofdisplayed data, decreases display of noisy data, and/or reduces datagaps and/or early shut off as compared to conventional sensors.

Calibration Routines

As noted above, it is desirable to provide a more convenient calibrationroutine for users, and especially for type II users or for those usingthe system for weight loss optimization and/or sports and fitnessoptimization.

One way of reducing the need for user-based calibration is by employingmore enhanced factory calibration, and certain details about methodsassociated with factory calibrations may be found in U.S. Ser. No.13/827,119, filed Mar. 14, 2013, published as US 2014-0278189-A1; andU.S. Ser. No. 62/053,733, filed Sep. 22, 2014, both of which are ownedby the assignee of the present application and herein incorporated byreference in their entirety.

Other techniques may also be employed to ease calibration requirements.For example, referring to the flowchart 145 of FIG. 17, if a priorsensor session showed generally reliable results (step 147), the samecalibration parameters may simply be employed from the prior sessioninto a new sensor session (step 149). In particular, the calibrationparameters from the old sensor session may be transmitted to the newsensor session in a number of ways, e.g., by employing the glucosesignal transmitter technology, if the calibration parameters are storedon the sensor electronics, or by passing the calibration state variableson to the new session if the calibration parameters are stored in themonitoring device, e.g., smart phone. This technique may be particularlyuseful if the sensors are in some way related, e.g., from the samebatch, the same package, of the same type, or the like.

This technique is not necessarily limited to just the use of a singleprior sensor session, e.g., going back just one session. For example,repeated patterns, e.g., historic patterns, may be learned by analysisof several prior sensor sessions. For example, the system may learn thatthe user is accustomed to eating pizza on Friday, and has done so overthe last seven sessions, and an algorithm may thus learn to not treatsuch events as outliers. Patient habits could also be learned, e.g.,that the patient likes to eat many small meals versus just a few largeones. Failure modes could also be learned, e.g., if the patient tendstowards a particular failure mode due to a particular way of installingand/or using their device. As discussed below in connection with FIG.18, certain glucose trace characteristics, constituting repeatableevents, may be advantageously learned from prior sensor sessions, and inmany cases multiple prior sensor sessions are necessary to distinguishcommon events from outliers. In addition, where other event data isavailable, e.g., meal or exercise data, correlations between suchrepeatable events and the entered meal or exercise data may be learned.

Next, referring to FIG. 18, a flowchart 178 is shown for another methodof calibration. In particular, it is known that certain glucose tracecharacteristics are indicative of repeatable events that, if they recur,recur at known and repeatable glucose values. As examples, steady-statevalues, certain trend values including certain slopes, and so on, tendto be reproducible for a given patient. Such repeatable events tend togive rise to repeatable and detectable characteristic glucose tracesignatures and/or patterns. For example, it is characteristic of manybiological systems that analyte values, if not changing rapidly, i.e.,are at a steady state, are highly reproducible. In other words, if ananalyte value is at a steady state over a first time, and subsequentlyis at a steady state over a second time period, then the value of theanalyte, e.g., concentration, is generally at or near the same value, ineach of the steady-state time periods. This concept can be employed tocalibrate analyte sensors.

For example, if a user is in a steady state with respect to an analyte,the value of the analyte can be measured and stored. When the user isagain at a steady state, it is highly likely their analyte value is thesame as the value measured previously, and thus a sensor, reading theanalyte concentration value, can be calibrated.

Different analytes may achieve different steady states in various ways.For example, uric acid concentration changes very little throughout atypical day. If the user has not exercised and not eaten for severalhours, their glucose value may be at a steady state. If the user has notexercised for several hours, their lactate value may be at a steadystate. In general, if a biological system has not significantly changedstate over a period of time, many analyte values, including glucose,will achieve a steady state. As noted the steady-state value isreproducible, especially for pre-diabetics, or nondiabetics, as well asfor those who are using the system primarily for weight lossoptimization or sports optimization. Thus, whenever a steady-state isdetected in an analyte value, the sensor measuring the analyte can becalibrated.

In some cases the system may prompt a user to fast from food or exercisein order to achieve a steady-state, which can then be measured andemployed subsequently for such calibrations. Moreover, the system maydetect a steady-state but prompt the user for verification, e.g., byasking the user “have you been fasting?”.

In some cases, a steady-state value may be determined based ondemographics of the user, thus not requiring any measurement at all. Forexample, for nondiabetics a typical glucose value may be between about80 and 100 mg/dL. In many cases, if a person is very nondiabetic theirvalue may be about 80, while if they are progressing toward prediabetestheir value may approach 100. Thus just providing the system withcertain information about a user may allow a degree of calibration to beperformed, particularly for certain applications, including where theuser does not need to have accuracy determined to a precise value, butrather where accuracy only to a particular range is sufficient, e.g.,hypoglycemic, hyperglycemic, or euglycemic.

Besides steady-states, other repeatable events that may be employedinclude slopes, responses after typical or similar meals, e.g.,responses after breakfast if the user eats the same sort of breakfastevery day. Other repeatable events include a range of low measurementsto high measurements, e.g., on a daily basis, i.e., a daily high to lowrange, certain types of excursions, certain types of transient patterns,decay rates and slopes, rates of change, and the like.

As a particular example, if a user has a characteristic breakfast, andthe characteristic breakfast leads to a characteristic postprandialglucose trend, then if a change in the trend occurs, it may be assumedthat, at least to some degree, a change in sensitivity of the sensor hasoccurred and the sensor needs to be recalibrated.

Additional data may be employed to help the calibration as well. Forexample, if the user is a diabetic and is measuring their blood glucoseseveral times a day anyway, such values may be employed as calibrationvalues. This is particularly true if their blood glucose value variessignificantly. In this case, if the system detects a local steady-state,the system can prompt the user to measure and enter a blood glucosevalue in order to correlate a value with the steady-state.

Historical data may in some cases be employed to determine steady-statecalibration values, e.g., prior data from blood records, data from aprior session, estimations from measurements such as A1C that track longterm glucose averages, or the like.

In some cases it is not necessary to hone in on a particular value.Determination that a user is in a range of values may be sufficient fortype II users. The range may be determined and used in providing theuser with information about whether goals are being met, or otherinformation about the program they are on.

Use of steady state assumptions may be employed not just for initialcalibration but also for update calibrations. That is, whenever thesystem is seen to be in a subsequent steady-state, the value measuredduring the subsequent steady-state may be assumed to be that determinedoriginally for the user. Other calculations may also be employed,including using weighted averages, slow moving averages (see below), andthe like.

Referring to FIG. 18, the method of flowchart 178 allows the calibrationof glucose concentration or other analyte values using informationgenerated directly from the device, i.e., the analyte concentrationsignal, itself.

Thus, referring to the flowchart 178, a first step in a calibrationroutine according to these principles is to detect that a system is in asteady-state (step 180), e.g., a first known steady state.

Commonly a steady-state may be detected without any action by the userat all (step 182). However in some cases a steady-state may be promptedby waiting a predetermined period of time after an event to allow asteady-state to be achieved (step 184), e.g., by waiting a predeterminedperiod of time following a meal or exercise routine (step 186). Thesystem or routine may ask or prompt for user information, e.g., aboutfasting, e.g., prompting the user to enter a duration since their lastmeal or related parameters. In the case of glucose the routine may beconfigured to look for a low rate of change, or a rate of change below apredetermined value, e.g., less than 0.25 mg/dL per minute. In somecases, where calibration has not yet occurred, the rate of change may bebased on counts or microamps or other “raw” signal value. The rate ofchange may be determined by calculation of a derivative.

Once a steady state has been detected, calibration parameters such assensitivity may be determined (step 188) based on the measured number ofcounts (or on current) at the steady state and the known steady stateglucose value, which may be known or assumed. Subsequently, anothersteady state may be detected (step 192), and where drift has occurredthe second steady state will be associated with a different number ofcounts. The different number of counts may be used to determine a degreeof drift, and the system may be recalibrated (step 194) using the new(second) number of counts measured at the new (second) steady state,along with the previously-known steady state glucose value.

In some cases, where the change is substantial, e.g., exceeds apredetermined threshold, the user may be prompted for additional data(step 196), e.g., a finger stick, data about exercise or meals, and soon. The difference may also be employed to determine a cause of thedrift (step 198).

A particular example is now described. A continuous glucose monitor maybe used for a patient without diabetes. In patients without diabetes,their glucose values are typically in the 70 to 90 mg/dL range. Theirglucose level deviates from this range only after meals or duringextreme exercise, and in some cases such deviations may be detected bychanges in the sensor signal or via auxiliary measurements using heartrate monitors and accelerometers. During these glucose excursions, thecurrent measured by the sensor will be changing rapidly which can beeasily detected by the monitoring device. The rate of change can becalculated using, e.g., an FIR filter over the last 20 minutes ofglucose values, but can be extended to a simple rate of change asdefined as the difference between two glucose values, at the start andend of a time period, divided by the duration of the time period. Duringrapid rates of change, the systems and methods according to presentprinciples, if using steady-state occurrences as a repeatable event forcalibrations, can avoid using such rapidly-changing data incalibrations, but rather may wait until the value is stable beforeperforming a calibration event (as noted certain calibration methods maytake advantage of such—e.g., as noted above, in some cases a repeatableevent usable for calibration may include transient noise events orpatterns—i.e., certain aspects of high variation areas or peaks may beuseful for non-steady-state repeatable event calibrations, and in manycases transient events, including rapid or slow rates of change ofanalyte concentration values, may form signal characteristics from whichpatterns are deduced and used as repeatable events). In one example, anabsolute rate of change threshold may be set at 0.25 or 0.5 mg/dL/minuteover the last 25 minutes of glucose data. As above, uncalibrated unitsmay also be employed. Thus, calibration may be prohibited if theabsolute rate of change threshold is exceeded. In other implementations,different calibration values may be used, and the same may also beconfigured to depend on the direction of rate of change, or thecalibration value used could be a function of the rate of change itself.

Referring to FIG. 19, and as noted with respect to step 194 of FIG. 18,steady states may be used to update calibration values as well as todetermine initial ones. Moreover, the system may be employed even ascalibration parameters change, e.g., as a sensor enzyme layer is alteredover time with use, or as other drift occurs. For example, referring tothe graph 202 of FIG. 19, the value of an analyte may be associated witheither a calibrated value or uncalibrated value at a time t₀ (axis 206),and this steady-state value may be assumed to be reproducible wheneverthe user is at a steady-state in that analyte value. This knowledge canlead to an initial calibration line 208, which slope is the sensorsensitivity if the axis 204 has units of counts. At a subsequent timet₁, which is also assumed to be in the steady-state, even if the numberof measured counts is different, knowledge of the same steady statevalue allows a subsequent calibration line 210 to be drawn and thus thesystem can be recalibrated. The degree of recalibration needed may behighly useful in the determination of the cause and treatment of drift.

Systems and methods according to present principles may be mosteffective when the baseline or background signal is sufficiently stableand predictable (or eliminated through advanced membrane or sensortechnology). When the sensor starts up, a current is generated. If thebaseline is sufficiently small or estimated with sufficient accuracy,then the remaining current will be from the analyte of interest. Thealgorithm may measure the current over a set period of time and if thecurrent is stable, e.g., within prescribed limits, then the algorithmmay assume that the glucose (or other analyte) is not changing and iswithin a narrow range. The algorithm may then calibrate the deviceautomatically using a glucose value of approximately, e.g., 80 mg/dL (orwhatever is determined to be the typical value for the user) andcorrelating that to the current that is generated during that time withstable glucose. In one implementation, a glucose value was set at 100mg/dL (it is reiterated that the same can change depending on factorssuch as rate of change, duration of wear, time of day, characteristicsof the user, e.g., state of progression toward diabetes, and so on). Thesystems and methods may employ a regression model to calculate the slopeand baseline with two points. The first point being a current generatedduring the stable glucose (and the approximated glucose level of a userwithout diabetes) and the second point being zero glucose (using anestimated value for the background signal). The slope of the line may bedetermined using, e.g., a weighted average, of a regression slope(counts/assumed to BG) and the previous slope estimate. Subsequent tothe calibration, glucose data may be presented to the user. The baselineof this implementation was assumed to be zero, however, a differentnonzero baseline value could be used. Calibration may also be updatedperiodically, e.g., every few minutes or every few hours, depending onimplementation.

The above technique may be employed in combination with factorycalibration information generated during the manufacture of the deviceor it could also be used with externally-generated glucose information,and may further account for changing sensitivity over time byincorporating pre-prescribed drift curves or other drift compensationtechniques, as described in greater detail in U.S. application Ser. No.13/446,848, filed Apr. 13, 2012, and published as US 2012-0265035-A1,owned by the assignee of the present application and herein incorporatedby reference in its entirety.

Systems and methods according to present principles may further beemployed to use calibration information about one sensor to calibrateanother, e.g., an adjacent sensor, e.g., one under the same membrane.Such calibration may be performed as drift parameters, if caused by themembrane, may be assumed to be the same for both sensors. For example,if both sensors are under the same membrane layer, e.g., a glucosesensor and a lactate sensor, and if one or more calibration parameterswere determined ex vivo, then the calibration parameters may be assumedto bear a similar relationship in vivo, and thus the measurement of onecan be used to determine the other. For example, if the lactate sensorhas a known offset in calibration from the glucose sensor (or otherrelationship or scaling or correlation factor), as measured ex vivo,then in vivo, a determination of calibration for that glucose sensor maybe employed to calibrate the lactate sensor. For example, if calibrationof the glucose sensor is seen to drift by 50%, then the calibration ofthe lactate sensor may be assumed to have drifted by 50%. Consequently,an update of one or more calibration parameters of one sensor can resultin the update of one or more calibration parameters of the other sensor.

Additional details of such aspects may be seen in U.S. application Ser.No. 12/770,618, filed Apr. 29, 2010, published as US-2011/0004085-A1;and U.S. Ser. No. 12/829,264, filed Jul. 1, 2010, published asUS-2011/0024307-A1, and [545PR], all of which are owned by the assigneeof the present application and herein incorporated by reference in itsentirety.

In addition, systems and methods according to present principles may usefactory calibration information to start and then incorporate anautomatic calibration technique over time to get more accurate glucoseinformation. If the signal did not follow pre-prescribed parameters, orwas outside of pre-prescribed parameters, the system could request acalibration value using known techniques, e.g., SMBG or finger stickcalibrations. The systems and methods may then incorporate this glucoseinformation into the original parameters to adjust the setpoint from,e.g., 100 mg/dL, to a more appropriate and accurate value. That is,while the above techniques intended for use in certain applications maybe generally configured to avoid the need for finger stick calibrations,if such are available, systems and methods according to presentprinciples may apply the same advantageously, for calibration purposesand otherwise.

Systems and methods according to present principles may be configured todetermine a confidence level or range, and as the resolution or accuracyof the data changes, the confidence level or range can change. In moredetail, the display could generate a value and a trend graph or the samemay show a range or other UI element. The range may change over time andshrink or expand as a confidence in the accuracy changes. For instance,during initial warm-up, a factory calibration value may be utilized.However, its accuracy may not be as precise as it would be withadditional information. During this time, the display may show a rangerather than a value.

A further feature of systems and methods according to present principlesare that they may request information when a user is set up within thesystem and adjust which technique to use depending on the information.For example, the system may prompt the user to enter whether they havetype I diabetes, type II diabetes, or are nondiabetic, and may select adifferent technique depending upon the answer. Systems and methodsaccording to present principles may further ask if the user isinterested in weight loss optimization, sports and fitness optimization,or other like optimization routines, and may adjust the algorithmaccordingly. The device may also, e.g., be used in a “blinded” mode foran extended period of time, e.g., 14 days, and only accept blood glucosevalues. These blood glucose values could be used to learn what apatient's resting blood glucose is, which could better guide theassumption of the auto calibration steady-state blood glucose value.After the extended period of time, the user could then use the device inauto calibration mode.

Besides the use of properties of the steady state value of analytes toglean additional information, “slow moving averages” may also beemployed, i.e., average values taken over, e.g., 1-3 days, as such slowmoving averages are also generally constant, particularly over the useof a sensor session. Thus, variations in the same can be used bothqualitatively and quantitatively to detect and quantify drift. Forexample, values of a slow moving average of a glucose concentration areshown by the graph 220 in FIG. 20 and the more schematic graph 212 inFIG. 21, where sensor counts are shown on axis 214 versus time on axis216. As may be seen, a slow moving average G1 measured at time t₁ maydecrease to a slow moving average G2 at time t₂. The slow moving averagemay be used to quantify drift because the selectivity to glucose of anadvanced sensor is high, therefore, the only thing contributing to thechange in the slow moving average is the sensitivity.

While details of the use of slow-moving averages are described in detailbelow, it is noted here that the same need not be of a contiguous periodof time. For example, a slow moving average may be taken by sampling acommon time period over a number of days. For example, a slow-movingaverage may be taken of nighttime time periods, and such may thenconsider only, e.g., an 11 pm-7 am time period. The slow moving averagewould then constitute an average of data taken only during this timeperiod, but over the course of several days. Other exemplary timeperiods in which such discrete or non-contiguous slow moving averagesmay be taken may include, e.g., post-prandial time periods, and so on.Such events could be time-based where the user has a very consistenttiming of such events, or alternatively event based, e.g., where eventsare marked or tracked by one or more sensors. For example, an exerciseevent could be marked by an accelerometer, a meal event could be markedby detecting a glucose spike, and so on.

In more detail, in lieu of the use of a daily average, a time period maybe employed over which an average is taken that is specific to aqualitative or quantitative type of time period that is specific andimportant to a user, e.g., making specific the time window looked atover which the average is taken. For example, a time window may beconsidered of “four hours after meals”. The time period data can be usedas measured over a number of days, but only looking at that particulartime period, and thus only measuring variations from the average of theglucose response during that particular set of time periods. Put anotherway, the average may be determined by stitching together and averagingall of the glucose values from the individual time periods over thecourse of several days. In this way, if a drift is detected, the same isdefined with respect to the average obtained by taking an average oversuch similar time periods. For example, a patient's daily average may be100, but their nighttime average may be 85, and their “regular” wakingdaytime average is 120. Measurements of drift may be taken with respectto this defined “localized” average. Exemplary time periods may include,e.g., after dinner, 9 am to noon, sleeping, and so on.

The slow moving average may be taken of calibrated or even uncalibratedvalues, though by itself a slow moving average may need additional datato perform an initial calibration. For this reason implementations mayinclude determining an initial glucose value using other methods, andobtaining, e.g., a days' worth of data, from which an initial slowmoving average may be determined and then compared tosubsequently-measured slow moving averages to determine, e.g.,sensitivity drift corrections and the like. Such systems and methods maybe particularly beneficial as the slow moving average can be checkedvery often compared to prior systems, e.g., every 5 minutes, as opposedto SMBG calibrations, which can only be done as often as the user iswilling to take the reading.

The flowchart 222 of FIG. 22 illustrates one implementation of the useof slow moving averages. In a first step, analyte values are measuredusing a sensor (step 224). A first slow moving average may then bedetermined (step 226). Analyte values may then continue to be measured(step 228), and the same may form the basis for displayed values of theanalyte concentration (step 232), where the displayed values are basedon an initial (or subsequent) value of the sensitivity. Second andsubsequent slow moving averages may be determined (step 230), where theperiod of the slow moving average is generally greater than ½ day, e.g.,1-3 days. The slow moving averages may be taken as often as desired,e.g., every 5 minutes, every hour, and so on. In some implementationsthe time constant of the filter may be changed, e.g., if the user ishaving an actual high, and thus the effect (the high analyteconcentration of the user) does not represent an actual drift or changein sensitivity of the filter. The use of a slow moving average may alsobe replaced by other forms of filtering such as order statisticfiltering or time domain filtering.

If the first and second values (or second or subsequent values, orindeed any set of values) of the slow moving averages varies, then inone implementation a drift may be assumed to have occurred, andcalibration may be adjusted (step 234) based on the drift, e.g., thedifference between the slow moving averages. Implementations describedbelow discuss other potential causes of variations in a slow-movingaverage. The display may be updated based on the recalibrated sensor(recalibrated based at least in part on the measured drift) (step 236).In some cases, the seed value (or other value used to base the display)may also be modified (step 238) to reflect (and compensate for) thedrift.

Referring to the flowchart 240 of FIG. 23A, recalibrations based on datagleaned from the change of a slow moving average (or otherrecalibrations) may be also be used in post-processing to update thedisplay of historic, here defined as “already displayed”, values basedon the recalibration. In a first step, values of an analyteconcentration may be measured by a sensor (step 242). The valuesmeasured may be displayed as clinical values based on a calibration,e.g., based on a previously-determined value of sensitivity (step 244).The sensitivity may change based on sensor signal information (step246), including based exclusively thereon, e.g., based on a change in aslow moving average or steady state value. The display may then beupdated based on the change (step 248), and in particular historicalready-displayed values may be redisplayed based on the recalibration,such that the historic values are represented more accurately. Otherforms of detecting sensitivity changes (or drift) using signalcharacteristics (or features) include CV (coefficient of variation),standard deviation, or inter-quartile range of the signal and itsrelationship corresponding parameter of glucose.

Once a change has been detected, the same may be analyzed or‘discriminated’ to determine the cause and/or magnitude of the change.It is common to find changes in sensitivity due to drift, but the samemay also have other causes, including pump problems, e.g., blockedtubes, or other problems, e.g., membrane breaches, or the like. And itis further desirable to distinguish these changes from those due toactual changes in glucose value. At least as a first step in this latterdetermination, measured changes in glucose values can be compared tothresholds for such changes which are physiologically feasible. If thechange is not physiologically feasible, then the change may beconsidered to be, at least in part, due to a drift or systemmalfunction.

Another way to discriminate signal drift behaviors is by comparing asignal drift curve to known signal drift curves, and in particular to aplurality or envelope of such curves. FIG. 4 illustrates one such curve,but for a given type of sensor, an envelope of such curves exists, i.e.,there exists a pattern to how sensitivity changes. If the way in whichthe sensitivity is changing follows one of these curves, then it may beinferred that the change is due to a sensitivity drift and not an actualglucose value change or a system malfunction. Additional details aboutsensitivity profile curves are described in, e.g., U.S. patentapplication Ser. No. 13/796,185, entitled “Systems And Methods ForProcessing Analyte Sensor Data”, filed Sep. 19, 2013, owned by theassignee of the present application and herein incorporated by referencein its entirety.

If the sensitivity changes by shifting to another of the knownsensitivity curves, known calibration parameters for that curve may beemployed in subsequent data analysis and display. If the sensitivitychanges outside of the bounds of the known sensitivity curves, then itmay be inferred that the changes are due to system issues ormalfunctions as noted above, e.g., errors or artifacts. However, incertain implementations, a certain sensor may have sensitivity curvesthat are known to be within a band. Known failure modes may cause thesensitivity to shift, either to another curve within the band or toanother band, i.e., the sensitivity may shift in a known failure mode toone of a known separate discrete band of curves.

It is noted in this regard that generally sensors of a given type willbe functional to meet required goals up to a certain tolerance. Forexample, 80% of sensors in a lot may work as desired (see FIG. 23B). Theremaining 20% may on the other hand (see FIG. 23C) not follow anexpected sensitivity curve. A large percentage, e.g., 75% of theseremaining sensors, may follow a known failure mode, which results intheir following a known alternate sensitivity curve, group of curves, orband of curves. By identifying which of these sensors are following thealternate sensitivity curve, and adjusting the calibrations of thesensors accordingly, the “failure” of the sensors may be remedied inlarge part. For example, if the failure mode is such that the 75% allhave signal values that tend to decrease in the same way, upondetermination of the failure mode, the “failure” may be remedied byadjusting the sensor readings “up”. Such aspects may be particularlyimportant as sensor sessions become longer and longer, e.g., go from 7sessions to 14 day sessions. In the failure mode illustrated in FIG.23C, a decrease in sensitivity began at about day eight. The ability todetect and remedy such failure modes is particularly important because,even as start of session sensor failure modes are becoming increasinglybetter characterized, end of session sensor failure modes, particularlywith longer sessions, remain difficult to quantify.

In some implementations, considerations of glucose signal variability incombination with a slow moving average may be employed to differentiateglucose signal variations from sensor sensitivity fluctuations. Forexample, if the slow moving average decreases but the variability staysthe same, or stays within a predefined range or band, then the cause ofthe slow moving average decrease is likely a sensitivity change.Alternatively, if the slow moving average decreases but the variabilityalso decreases, then the cause of the decrease is likely a real andactual change in the glucose concentration.

Non-physiologically feasible changes, variations, errors, artifacts, andother signal behaviors may be the cause of various remedial actions bythe system, and some of these have been mentioned above. For example,recalibrations may be performed, and the results may be propagatedbackwards to historical data. The user may be prompted to provide afingerstick calibration. Part of a remedial action algorithm may be todetermine whether to correct via recalibration or whether to prompt fora fingerstick or other calibration point. For example, if a signal isreceived that is outside physiologically feasible boundaries, then theuser can be prompted for an additional calibration point, e.g., afingerstick. Alternatively, the user may be prompted to provideadditional information of other sorts, e.g., to enter data correspondingto recent exercise or meals, or other recent changes in user behavior.As a specific example, if a user's slow moving average of glucoseconcentration was 100 mg/dL for the first three days of a session, buton the fourth day it was suddenly 200, systems and methods according topresent principles may prompt for a finger stick calibration. If thefourth day slow moving average was 105 mg/dL, then the system may scaleor adjust the sensitivity accordingly, e.g., to bring the value backdown to 100 mg/dL.

Where finger stick calibrations are performed, the use to which thefingerstick calibration data is put may vary depending on the use of thedevice. For example, if the device is being used adjunctively, i.e.,non-therapeutically, which is the case for many type II users, if thefingerstick indicates a drift, it may still be possible to use thesensor if the drift is not substantial. In many cases, the techniquesdescribed here can be used to remedy the effect of the drift, and stillallow the display of an accurate reading. If the device is being usednon-adjunctively, e.g., for insulin using type I patients, then if thefingerstick indicates a drift, the remediation or adjustment, e.g.,recalibration, may be made more aggressively, and if such cannot bemade, or if an accurate sensor reading cannot be returned even uponrecalibration, then an indication may be displayed to the user to ceaseuse of the sensor.

Pattern analysis may be performed to determine if the change orvariation is of a known type, e.g., is characteristic of knownsensitivity changes. Pattern analysis may determine if changes orvariations meet criteria, e.g., exceed certain thresholds, known forcertain behaviors. As noted above a day's behavior may be employed inthe determination of a slow moving average. If after analysis that day'sdata is determined by the system to be untrustworthy, another day's datamay be used. Data may be displayed in ranges or bands, or withconfidence intervals or other indications, rather than as a highprecision numeral. Where slow moving averages are employed, the timeconstant of the slow moving filter may be adjusted so as to include orexclude more short term variations.

These aspects are summarized in the flowchart 250 of FIG. 24.

Referring first to FIG. 24, values of an analyte concentration may bemeasured by a sensor (step 252). Such values are generally measured as acurrent, e.g., as amps (pico-amps), and equivalently as counts. A slowmoving average may be defined by measuring the counts over a long periodof time, such as over several hours, a half day, 24 hours, or 2-3 days.As sensors vary from unit to unit, the slow moving average willgenerally only be meaningful once an initial period, e.g., 24 hours, haspassed. Thus, a next step is to determine a first slow moving averageover a first period of time (step 254).

In one implementation, a value for initial use of a first apparentsensitivity may be posited via a seed value as in a manner describedabove. A first apparent sensitivity may then be determined based on thefirst slow moving average (step 256). In particular, if a first slowmoving average is posited, then the first apparent sensitivity may bebased on the relationship between the posited first slow moving averageand the measured one.

Subsequently, a second slow moving average may be determined over asecond period of time (step 258). And again, optionally, a secondapparent sensitivity may be based thereon (step 260).

If the slow moving average, or apparent sensitivity, is seen to changebetween the first and second time periods, then remedial action may becalled for. Thus, a next step is to determine if the change matchespredetermined criteria (step 262). Predetermined criteria may include anumber of elements (step 270), e.g. known sensitivity changes over time,an envelope of sensitivity profile curves, physiologically feasiblechanges, changes associated with errors such as pump malfunctions, orthe like. For example, such a step may call for a determination as towhether a change matches criteria associated with a sensitivity drift, amalfunction, or an actual change in an average glucose concentrationvalue (step 264). If consistent with drift, e.g., if it is determinedthat sensitivity has drifted by comparison with known sensitivityprofile curves, then the correction may be made automatically. In anycase, the sensitivity may be adjusted based at least in part on thedifference between the two slow moving averages (step 268), as the sameprovides a quantitative indication of the degree of change or drift thesensor has undergone.

If the change is not consistent with drift, it may be determined if thechange is consistent with other causes for which predetermined criteriaexist. If the change is not consistent with known behaviors, e.g.,matches no predetermined criteria, then the user may be prompted toenter information to explain the change (step 266), e.g., meal orexercise information, a finger stick calibration value, data from otherexternal sources, or the like. In some cases, the user-entered data maybe employed, along with the quantitative difference in slow movingaverage or sensitivity, in a recalibration routine, e.g., to determine anew or updated sensitivity.

FIG. 25 illustrates another flowchart 286 of an exemplary methodemploying slow moving averages or steady state values. In a first step,after an initial calibration, analyte concentration values continue tobe monitored with a sensor (step 288). The initial calibration may bebased on a number of factors (step 290), including a population average,data from a prior session, bench data, in vitro data, or other a prioridata.

Based on the measurements and on the initial value, a clinical value ofthe analyte concentration is calculated and/or displayed. An updatedcalibration may then be calculated, based only on the measurements,e.g., only on the signal from the sensor (step 294). The adjusting maybe based on changes in the slow moving average, changes in steady statevalues, or other bases.

Subsequent to the updated calibration, clinical values may be calculatedand/or recalculated based on the updated calibration (step 298), and thedisplay may then be updated (step 300), including updatingpreviously-displayed (historic) values to updated values based on theupdated calibration.

Yet another implementation of present principles is illustrated by theflowchart 302 of FIG. 26. A first step is, on a monitoring device,receiving a seed value of a calibration parameter (step 304), e.g.,sensitivity. The seed value may be based on a number of factors (step306), including a user self-characterization of a disease state, apopulation average, data from a prior session, bench data, in vitrodata, or other a priori data.

The monitoring device then continues to receive sensor data, and maydetect when an analyte concentration value is at a steady state (step308). For example, this may occur when a set of received signals over apredetermined period of time is within a predetermined range or band ofvalues. A correlation may then occur of the measured signal value, e.g.,in current or counts, to the known or assumed steady state value (step310).

Subsequent to the correlating step, the monitoring device continues toreceive signals from the sensor (step 312). Clinical values of theanalyte concentration are calculated and displayed based on the receivedsignal, the known or assumed steady state value (even if the host is nolonger at the steady state), and the seed value (step 314).

Behavior may be detected outside of pre-prescribed parameters, asdescribed above in connection with FIG. 24, and users may be prompted toenter external data, e.g., a fingerstick calibration value (step 316). Arecalibration may be computed, and/or a recalculation, followed bysubsequent display (step 318), based on the received signal, theexternal data, and optionally the seed value. In some implementationsthe known steady state value, and/or the seed value, may be reset basedon the calculations performed (step 320), and historic valuesrecalculated and redisplayed.

FIGS. 27 to 33 illustrate a detailed method for determining calibrationparameters, e.g., sensitivity and baseline, using a probabilisticapproach. Certain aspects of probabilistic approaches are described inU.S. patent application Ser. No. 13/827,119, entitled “AdvancedCalibration For Analyte Sensors”, filed Mar. 14, 2013, owned by theassignee of the present application and herein incorporated by referencein its entirety. In this application incorporated by reference, whichincludes what is termed here a “signal based calibration algorithm”, apriori calibration distribution information is modified with real timeinputs and converted into a posteriori calibration distributioninformation, from which a calibrated data point is determined. In thisway, calibration errors may be avoided where, e.g., regression resultsin errant sensitivity and/or baseline values due to improper assumptionsabout reference data. Other ways of determining calibration parameterssuch as sensitivity and baseline may also be employed, besides the waysdescribed in the application incorporated by reference above. These waysinclude techniques enabling factory calibration usable during the lifeof a sensor session, and so on.

In FIGS. 27-33, distributions are again employed for calibrationparameters such as sensitivity and baseline, but the same are optimizedbased on subsequently-known data, e.g., a sensor count distributionobtained over the first 24 hours of use of a sensor session. Referringfirst to the flowchart 322 of FIG. 27, a first step is to receive aninitial value of an analyte concentration, from a sensor, and an initialvalue or distribution of sensitivity and optionally baseline (step 324).In some cases the effect of the baseline may be reduced to essentiallyzero, simplifying the calculations. The initial value of sensitivity canbe from sources noted above (step 326), e.g., entered by a user, drawnfrom a population average, transferred from a prior session, or othersources of seed values. The initial value may also be used as part of abasis or in a calculation for a slow moving average filter. Where theinitial values of sensitivity or baseline are distributions of suchvalues, then the same may at least initially be developed fromconsiderations of population statistics.

The analyte signal is then monitored from the sensor (step 328). Aplurality of clinical values are then calculated and displayed, based onthe monitored signal and on the initial value or distribution ofsensitivity (step 330) or alternatively on an initial value of averageglucose. For simplicity the baseline is assumed to be zero ornegligible. The initial value or distribution of sensitivity is positedso that the user can be provided with displayed analyte concentrationvalues, even if the same is less accurate during this initial timeperiod than it will be subsequently, once additional data is gleaned.

A distribution of values of the monitored signal may then be determined(step 332). A representative value of the distribution of values of themonitored signal may be calculated (step 334), e.g., an average value, amedian value, a mean value, and so on. As an initial sensitivity, therepresentative value may be divided by an initial value of analyteconcentration (step 338), based on the initial posited sensitivity.

The initial value or distribution of values of sensitivity and/or theplurality of concentration values may then be optimized to match thedistribution of values of the monitored signal (step 336). In moredetail, the sensor count is the product of the sensitivity and theanalyte concentration, and thus the concentration is equal to the sensorcount divided by the sensitivity. The median sensor count may bedetermined, e.g., after 1 day's data is obtained, and a search may beperformed that optimizes or provides the best fit for the distributionof the sensor count given the distributions or samples from asensitivity parameter space and a baseline parameter space. For example,if a user's long term glucose values over a day ranged from 100 to 200,certain limits may be deduced on what the sensitivity and baseline canbe. So the sensitivity and long term glucose values, withindistributions, may be selected such that their product best optimizesthe measured representative value of sensor counts. In addition, thesensitivity and long term glucose values may be selected (step 340) suchthat their values are ‘most likely’, where ‘most likely’ means thattheir values are closest to the centers of their respectivedistributions. A slow-moving average may be monitored (step 342), andchanges in the same detected and analyzed as noted above (step 344).

Put another way, following the first day, data exists pertaining to aposited initial average glucose value, or sensitivity, and adistribution of sensor counts. From the distribution, a median sensorcount may be obtained.

Sensor count SC=f_(SC)(SC), which has a normal distribution.

The sensitivity equation has the form:

y=mx+b

If it is assumed b=0 and the equation is further specified to averages,then:

Median sensor count=m*average glucose value

And considering both sensor count and sensitivity as normaldistributions:

f _(SC)(SC)=m*GV

Moreover, it is known that m is a slow moving function of time due todrift, and thus:

f _(SC)(SC)=m(t)*GV

And thus it is apparent that sensor count and glucose value areconnected by a multiplicative “constant”, which is actually a slowmoving function of time.

The distribution of sensor count can also reveal aspects of thepotential distribution of sensitivity, i.e., the potential initialsensitivity m, and in particular:

Average GV=(median SC/m _(median))

And thus:

m _(median)=median SC/average GV

For example, if the median SC is 131,000 and the average GV is 131, thenm is 1000 counts/(mg/dL). And the distribution of m may be checked todetermine if this value is reasonable or unlikely. And a similardetermination may be made for ‘b’, in cases where the same isnon-negligible.

The representative value of the values of the monitored signal (sensorcount) may be converted into an estimate of long term glucose:

Long term glucose=(long term sensor count)/(sampled sensitivity)−sampledbaseline in mg/dL

The graph 346 of FIG. 28 illustrates an exemplary distribution of samplesensitivities, the graph 348 from FIG. 29 illustrates an exemplarydistribution of sample baselines, and the graph 350 of FIG. 30illustrates an exemplary distribution of sampled long term glucosevalues. As one example, if a representative value of sensor count is113,536, then given the constraints of the above-noted threedistributions, the optimal slope is 890 counts/(mg/dL), the optimalestimate of long term glucose is 153.5685, and the optimal baseline is−26 mg/dL. These points are illustrated on the same set of graphs 346,348, and 350, reproduced on FIGS. 31, 32, and 33, at points 354, 358,and 362, respectively.

In variations, distributions may be made more ‘granular’, such thatdifferent distributions may be provided for different demographicpopulations or groups. Other variations will also be understood.

As noted above, a slow moving average filter may be employed as part ofdrift quantification because the selectivity to glucose of an advancedsensor is high, therefore, the primary factor contributing to the changein a slow moving average is the sensitivity. To seed the slow movingaverage filter initially, the initial seed value of the average glucoseconcentration may be multiplied by the average sensitivity to get aninitial number of counts. Subsequently:

S _(t)=αFilter_(t) α*S _(t-1)+(1−α)STX _(t)sensor_(t)

After a period of time, e.g., 1 day, enough data may be received suchthat the same can be revised to the actual average and the same employedfor determinations of drift. The above steps may then be repeated, todetermine subsequent best combinations of slope, baseline, and glucosein the manner described above will give the best raw count to match upwith the estimate of raw count determined from, e.g., day 1 data.

Distributions can be modified in some implementations according tomeasured data as more is obtained. In this way a better calibration maybe obtained. At the beginning only posited distributions were employed.Subsequently, actual measured data is available and may be employed.Filters may be reseeded with a median or other representative sensorcounts, and the distributions will generally converge to actual measureddata. If fingerstick data is available, the same may be employed foreven faster convergence.

The seeding or re-seeding may be performed in a number of ways, andtailored to enable a more rapid convergence of drift curves based onfilter seed parameters. For example, initial seed values for the filtercan be based on the expected signal level as estimated from an expectedaverage glucose level and sensor sensitivity. Initial seed values forglucose can also be based on subject demographics such as age of userand their duration of diabetes. Initial seed values can also be based ondata such as medical record information or laboratory tests such asfasting glucose levels, glycated hemoglobin (A1C) tests, currentdiabetes treatments (e.g. oral medications, basal insulin use, orintensive insulin therapy) or downloaded self-monitored blood glucosevalues.

In another implementation, an initial seed value can first be used tostart a filter running in the forward direction. After a representativeset of sensor readings has been collected, e.g., after 24 hours ofsensor readings, then a second filter can be run in the reversedirection. When a representative set of sensor readings is available,then the forward or reverse filters can be seeded with a typical signalvalue, such as the median sensor reading, or the filters can be seededwith typical signal values that are adjusted for the expected drift,such as starting the forward filter with 0.9*median and the reversefilter with 1.1*median. These techniques have the benefit that when twoor more filters are used, such as a forward filter and a reverse filter,then their seed values can be further optimized to minimize thedifference between the two filter outputs. For example, one exemplaryway is to minimize the mean squared error between the two filteroutputs.

In the system and method according to present principles as describedabove, redefining the seed values from day to day helps minimize themean absolute error in the signal domain. In one implementation, eachday, using a rough estimate of signal trajectory, e.g., from day 1, asmooth trend of a noisy filter output can create the trajectory fordrift for day 2. Drift rate estimates can be compared from two or moredifferent methodologies, and a difference or error between the two canfeed into an algorithm such as a signal based calibration algorithm suchas is disclosed in the patent application incorporated by referenceabove (Serial No. 13/827,119), that determines distributions ofsensitivities, particularly with regard to certainty intervals. Also inthis way, signal features can be extracted, including featurescorresponding to noise, level, drift model, power, energy, and so on. Inthis way, it can be determined whether the drift correction is on aproper trajectory. For example, if the drift slope is considerablydifferent than what a factory calibration model would suggest, e.g., bymore than a predetermined threshold percentage, then the user can beprompted for feedback or can be prompted to provide a finger stickcalibrations.

In some implementations, smoothing filters may be employed to compensatefor signal drift in real time. In one case, a double exponentialsmoothing filter is used. Such a filter may assume a multiplicativedamped trend with no seasonality; however, one may assume an additive ormultiplicative seasonality to improve performance. The doubleexponential filter operates to recover the underlying change in sensorsignal, i.e., drift, as a function of time. There are three primaryunderlying equations that govern the double exponential smoothingfilter:

ŷ_(t + h|t) = l_(t) * b_(t)^(⌀)l_(t) = α * y_(t) + (1 − α) * l_(t − 1) * b_(t − 1)^(⌀)$b_{t} = {{\beta*\frac{l_{t}}{l_{t - 1}}} + {( {1 - \beta} )*b_{t - 1}^{\varnothing}}}$

A table for the parameters in the equations can be seen below in TableI:

TABLE I Ø The dampening parameter (between 0 & 1) that dampens thetrend. This makes the trend approach a constant sometime in the future.l_(t) An estimate of the level of the series at time t b_(t) An estimateof the growth rate (in relative terms) of the series at time t α Thesmoothing parameter for the level, 0 ≦ α ≦ 1 β The smoothing parameterfor the trend, 0 ≦ β ≦ 1 y_(t) Input into the time series, e.g., afiltered sensor count ŷ_(t+h|t) The h-step ahead forecast of the sensorcount

In the above equations, and in this setting, alpha and beta may beconsidered to be small. Alpha is small because it is desirable to havethe filter not be influenced by glucose excursions. Beta is smallbecause the underlying trend being recovered is slow moving in nature.One set of exemplary results were generated using the followingparameters (Table II (assumes a five-minute sampling rate)):

TABLE II α 0.0006 β 0.001 Ø 0.1 b_(t−1) 1.01 l_(t−1) m * Mean Glucose

In the above equations, slope can be the algorithm-calculated initialsensitivity or a value determined from another methodology, or using anyof the methods for determining sensitivity values noted above. Meanglucose in the above equation can be the mean of historic glucose valuesfrom prior sessions or, e.g., based on A1C values reported by users. Inone implementation, data were generated using the initial sensitivityestimated by the algorithm using a 2 hour calibration and self-reportedA1C numbers from a cohort of test subjects.

A drift correction curve was estimated using the following equation:

${Drift}_{t} = \frac{{\hat{y}}_{t} - {\hat{y}}_{0}}{{\hat{y}}_{0}}$

The sensor signal was then drift corrected using the following equation:

${DriftCorrectedSensor}_{t} = \frac{{Sensor}_{t}}{1 + {Drift}_{t}}$

Glucose values were then calculated using the following equation:

${Glucose}_{t} = \frac{{DriftCorrectedSensor}_{t} - {baseline}}{slope}$

In the above equation, slope is that estimated by the algorithm at theinitial calibration and the baseline is the slope multiplied by 1 mg/dL.

To show the efficacy of the double exponential filter, FIGS. 34 and 35illustrate exemplary glucose traces. FIG. 34 shows a CGM trace 364 alongwith reference and calibration values. FIG. 35 illustrates a raw sensorsignal 366 along with an output 367 from a double exponential filter. Inthis case the sensor was calibrated once using two start-up valuesentered by the user.

The estimated drift curve for the above sensor can be seen by the curve368 in FIG. 36. As may be seen, the drift curve is readily seen in bothupward and downward directions, and knowledge of the drift as detectedand quantified by the double exponential filter allows for correction ofthe drift. One advantage of this implementation is that there is no needto rely on any known curve shapes to correct for drift.

FIGS. 37-39 are additional charts in which drift correction according tothe above principles is illustrated.

Besides the use of double exponential filters, other filters may also beused. For example, Kalman filters may be employed that include processnoise, also known as model noise, and measurement noise estimates.Gaussian filters may also be used, as well as classical Butterworthlow-pass filters, moving median filters, moving average filters, and soon, so long as the filter helps uncover an underlying trend. Filterbanks or series of filters may be used, that combine multiple filters soas to obtain an average or combined trend in an underlying signal. Wheremultiple filters are used, different types of filters may be employedwithin a single bank, and filter settings may vary from filter tofilter. By the use of multiple filters, signal correction may beimproved at the ends of subsequent time periods, e.g., at the end of asecond day's worth of data, at the end of a third day's worth of data,and so on. Without wishing to be bound by theory, it is assumed in theuse of these filters that a variability in an average glucosemeasurement from day to day is insignificant compared to the change inslope or underlying signal change.

In other variations, signals may be pretreated before drift estimationfiltering to remove data gaps and outliers. In addition, calibrationsmay be automatically updated in such a way as to reduce the occurrenceof unexpected jumps in CGM readings. Such ways include making changeswhen the signal is stable, e.g., altering calibration settings in themiddle of the night, or slowly blending the calibration changes into acurrent setting over a period of time, e.g., an hour or more.

Other useful techniques that may be employed with filtering includevarious decomposition techniques. For example, empirical modedecomposition can be employed to break down the signal into a series ofintrinsic mode functions over time. Other time and frequency baseddecompositions may be employed, including Fourier transforms and wavelettransforms.

In another variation, other signal-based parameters can be determinedand used in calibrations. For example, referring to FIG. 40, it may beseen that the coefficient of variation (CV) of the sensor signal has astrong correlation with the glucose variation in the signal, e.g., withthe glucose standard deviation. Thus, in determining calibrationparameters, this correlation may be employed to select calibrationparameters that satisfy the signal CV—glucose standard deviation errormodel.

In more detail, a priori information as has been described can beemployed in factory calibration and such includes information obtainedprior to a particular calibration. For example, such informationincludes that from previous calibrations, that obtained prior to sensorinsertion, and so on. Calibration information includes informationuseful in calibrating a continuous glucose sensor, such as, but notlimited to: sensitivity (m), change in sensitivity (dm/dt), and othersignal and time derivative aspects as have been described above. Alsoimportant a priori information includes distribution information, suchas ranges, distribution functions, and distribution parameters includingmean and standard deviation.

With respect in particular to the standard deviation of distributions ofglucose values, the same may advantageously be employed, e.g., indetermining boundaries of likely glucose values and probablecombinations of sensitivity and baseline. Standard deviations may alsobe employed in determinations of levels of certainty from a prioricalibration distribution information, such as where the same is afeedback of a posteriori calibration distribution information from aprior calibration (where the level of tightness or looseness of thedistribution is quantified by the standard deviation). In the same way,levels of certainty may be determined from a posteriori calibrationdistribution information, e.g., based on the level of tightness orlooseness of distribution, which again may be quantified by the standarddeviation.

As a particular example, FIG. 41 shows a glucose signal over the courseof 10 days. From this a signal standard deviation and a signal mean maybe computed. A signal coefficient of variation may then be determinedby:

Signal CV=Signal Standard Deviation/Signal Mean

In the case of FIG. 41, the signal CV was calculated to be 0.4230.

If a relationship has been determined between the signal CV and theglucose standard deviation, e.g., see the line in FIG. 42, then thecalculated signal CV determined above may be used to determine anexpected glucose standard deviation, which may then be used in thecalculations noted above, as well as other calculations. For example, anerror model may be built using this correlation and the error model usedin calibrations within the above incorporated by reference U.S. patentapplication Ser. No. 13/827,119.

FIG. 43 shows an exemplary distribution of the difference between themeasured standard deviation and the expected standard deviation.

Other relationships may also be employed. For example, referring to FIG.44, a relationship may be seen between the mean glucose value and theglucose standard deviation. In particular, it can be seen that userswith a higher standard deviation tend to have a higher mean glucose.This relationship may be employed in determining, setting, inferring, orotherwise choosing calibration values. As another example, and referringto FIG. 45, another useful indicator is patient type. In particular,FIG. 45 illustrates a clear distinction in glucose standard deviationbetween non-diabetics, type I diabetics, and type II diabetics.Accordingly, based on the type of patient, the model chosen for apatient population could be changed based on the type of population,e.g. the standard deviation could be tightened on the CV error model,the mean could be shifted, and so on.

As noted above, the system may adjust the data for a time lag (e.g., toremove a time lag induced by real-time filtering) from data for aprevious time period and may display a graphical representation of thetime lag adjusted data for that time period (e.g., trend graph). Thesystem may also compensate for a time lag. For example, FIG. 46 showdata points separated by time lags, wherein Δ represents the individualrate of change between two adjacent points. By use of an equation suchas the below or similar, such time lags may be compensated for:

Glucose(t)=[DriftCorrectedSensor(t)+5*ROC(t)]/m−b (mg/dL)

For example, if the current point is in light noise, all filtered sensorcounts may be employed.

Example

An exemplary calibration routine is now described, steps of which may beseen in the flowchart 400 of FIG. 47. In a first step, a sensitivityprofile versus time is characterized with a bench test that measures thesensor's response in a glucose solution for one or more days (step 402).As this is commonly a destructive test, the same may be run on arepresentative set of sensors from a manufacturing lot or manufacturingline. This test may be repeated periodically or when the processchanges, such as when there are changes in the raw materials orequipment. The sensitivity of each sensor may then be measured with anondestructive bench test (step 404). The results of steps 402 and 404are used to estimate the in-vivo initial and final sensitivity of eachsensor (step 406).

It is noted here that the destructive tests measure the long-term driftfor a group of sensors, e.g., determining that the sensors drift 10%across the first two days. The nondestructive bench test provides astarting point for each sensor in the log, e.g., that a subject sensormay have a starting sensitivity of 20. Combining the two tests, it canbe determined that, e.g., the subject sensor starts at 20 and isexpected to drift 10%, e.g., to 22.

Thus, this step maps or transforms bench values into in-vivo values witha function that was trained or optimized on well characterized in-vivodata, like clinical trials. As another example, the subject sensor maystart at 24 and drift to 26 in-vivo.

The transmitter's electrical characteristics (such as gain and offset)are calibrated during manufacturing (step 408) and these calibrationfactors are stored on the transmitter (step 412) so that the raw sensorsignal can be corrected for part-to-part differences in the electronicsbefore running the algorithm.

A sensor is then packaged with a single-use transmitter (step 414). Thesensor's identifier, e.g., identification number, is read with anoptical bar code and its estimated in-vivo sensitivity are retrievedfrom a manufacturing database and written to the transmitter using,e.g., wireless (NFC or Bluetooth®) communication (step 416).

When the transmitter first detects that it is connected to a functioningsensor a session timer is started and the algorithm starts (step 418).The algorithm starts converting the sensor signal to glucose (step 420)using the CGM model with model parameters set to the prior informationabout sensor sensitivity recorded in step 416.

When a representative set of signal data, e.g., 24 hours, is available,the seed parameters for forward and reverse filters may be set using themedian signal value and assumed drift amount (step 422). These seedvalues are then further optimized to minimize the mean squared errorbetween the two signal filters (step 424).

A signal-based calibration algorithm uses the average of the forward andreverse filter signal and the also the raw sensor signal, and in sodoing, adjusts the model parameters, e.g., sensitivity and baseline, tomeet several criteria (step 426). In so doing, time-based input data isemployed to update the algorithm. An exemplary signal-based calibrationalgorithm is that disclosed in the patent application incorporated byreference above (Serial No. 13/827,119), and in particular at [0188],i.e., Example 4, which illustrates a Bayesian learning approach fordrift estimation and correction.

In the implementation of FIG. 47, the model is adjusted to meet acriterion that the mean glucose value is consistent with the expecteddiabetic mean, and may further be adjusted to meet another criterionthat the CGM glucose variability is consistent with the mean glucoselevel. To calculate these metrics, the algorithm has an assumedrelationship between mean glucose and glucose variability. For example,a nondiabetic can have a mean glucose level of 85 mg/dL and a standarddeviation of 15 mg/dL. A diabetic could have a mean glucose of 170 mg/dLand a standard deviation of 65 mg/dL.

The optimized model parameters are used by the CGM model to convertsubsequent sensor readings into glucose values (step 428), which arethen displayed.

Steps 424 to 428 are repeated when a new set of signal data isavailable.

A similar method may be used to detect an unacceptable amount of sensorchange (typically a loss of sensitivity from day 7 onwards) and to stopdisplaying potentially inaccurate readings.

In one preferred embodiment, the analyte sensor is an implantableglucose sensor, such as described with reference to U.S. Pat. No.6,001,067 and U.S. Patent Publication No. US-2005-0027463-A1. In anotherpreferred embodiment, the analyte sensor is a transcutaneous glucosesensor, such as described with reference to U.S. Patent Publication No.US-2006-0020187-A1. In still other embodiments, the sensor is configuredto be implanted in a host vessel or extracorporeally, such as isdescribed in U.S. Patent Publication No. US-2007-0027385-A1, co-pendingU.S. patent application Ser. No. 11/543,396 filed Oct. 4, 2006,co-pending U.S. patent application Ser. No. 11/691,426 filed on Mar. 26,2007, and co-pending U.S. patent application Ser. No. 11/675,063 filedon Feb. 14, 2007. In one alternative embodiment, the continuous glucosesensor comprises a transcutaneous sensor such as described in U.S. Pat.No. 6,565,509 to Say et al., for example. In another alternativeembodiment, the continuous glucose sensor comprises a subcutaneoussensor such as described with reference to U.S. Pat. No. 6,579,690 toBonnecaze et al. or U.S. Pat. No. 6,484,046 to Say et al., for example.In another alternative embodiment, the continuous glucose sensorcomprises a refillable subcutaneous sensor such as described withreference to U.S. Pat. No. 6,512,939 to Colvin et al., for example. Inanother alternative embodiment, the continuous glucose sensor comprisesan intravascular sensor such as described with reference to U.S. Pat.No. 6,477,395 to Schulman et al., for example. In another alternativeembodiment, the continuous glucose sensor comprises an intravascularsensor such as described with reference to U.S. Pat. No. 6,424,847 toMastrototaro et al.

The connections between the elements shown in the figures illustrateexemplary communication paths. Additional communication paths, eitherdirect or via an intermediary, may be included to further facilitate theexchange of information between the elements. The communication pathsmay be bi-directional communication paths allowing the elements toexchange information.

The various operations of methods described above may be performed byany suitable means capable of performing the operations, such as varioushardware and/or software component(s), circuits, and/or module(s).Generally, any operations illustrated in the figures may be performed bycorresponding functional means capable of performing the operations.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure (such as the blocks of FIGS. 2and 4) may be implemented or performed with a general purpose processor,a digital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array signal (FPGA) or otherprogrammable logic device (PLD), discrete gate or transistor logic,discrete hardware components or any combination thereof designed toperform the functions described herein. A general purpose processor maybe a microprocessor, but in the alternative, the processor may be anycommercially available processor, controller, microcontroller or statemachine. A processor may also be implemented as a combination ofcomputing devices, e.g., a combination of a DSP and a microprocessor, aplurality of microprocessors, one or more microprocessors in conjunctionwith a DSP core, or any other such configuration.

In one or more aspects, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage media may be anyavailable media that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise varioustypes of RAM, ROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to carry or store desired program code in the form ofinstructions or data structures and that can be accessed by a computer.Also, any connection is properly termed a computer-readable medium. Forexample, if the software is transmitted from a website, server, or otherremote source using a coaxial cable, fiber optic cable, twisted pair,digital subscriber line (DSL), or wireless technologies such asinfrared, radio, and microwave, then the coaxial cable, fiber opticcable, twisted pair, DSL, or wireless technologies such as infrared,radio, and microwave are included in the definition of medium. Disk anddisc, as used herein, includes compact disc (CD), laser disc, opticaldisc, digital versatile disc (DVD), floppy disk and Blu-ray® disc wheredisks usually reproduce data magnetically, while discs reproduce dataoptically with lasers. Thus, in some aspects a computer readable mediummay comprise non-transitory computer readable medium (e.g., tangiblemedia). In addition, in some aspects a computer readable medium maycomprise transitory computer readable medium (e.g., a signal).Combinations of the above should also be included within the scope ofcomputer-readable media.

The methods disclosed herein comprise one or more steps or actions forachieving the described methods. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

Certain aspects may comprise a computer program product for performingthe operations presented herein. For example, such a computer programproduct may comprise a computer readable medium having instructionsstored (and/or encoded) thereon, the instructions being executable byone or more processors to perform the operations described herein. Forcertain aspects, the computer program product may include packagingmaterial.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit and each intervening value between the upper and lower limitof the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention, e.g., as including any combination ofthe listed items, including single members (e.g., “a system having atleast one of A, B, and C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). In those instanceswhere a convention analogous to “at least one of A, B, or C, etc.” isused, in general such a construction is intended in the sense one havingskill in the art would understand the convention (e.g., “a system havingat least one of A, B, or C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Headings are included herein for reference and to aid in locatingvarious sections. These headings are not intended to limit the scope ofthe concepts described with respect thereto. Such concepts may haveapplicability throughout the entire specification.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

What is claimed is:
 1. A method of calibrating an analyte concentrationsensor within a biological system, using a signal from the analyteconcentration sensor, wherein at a steady state, the analyteconcentration value within the biological system is known, comprising:on a monitoring device, receiving a seed value of a calibrationparameter; on the monitoring device, detecting when an analyteconcentration value as measured by an analyte concentration sensorindwelling in a biological system is at a steady state; and on themonitoring device or on a device or server operatively coupled to themonitoring device, correlating a measurement of the analyteconcentration value when the biological system is at the detected steadystate to the known analyte concentration value; subsequent to thecorrelating, receiving a signal from the sensor; and calculating anddisplaying a value corresponding to the received signal, the calculatedvalue based on the received signal, the known analyte concentrationvalue, and the seed value.
 2. The method of claim 1, wherein thereceived seed value is received from a source including factorycalibration information.
 3. The method of claim 1, further comprising:detecting a behavior in the received signal outside of a pre-prescribedparameter; and prompting a user to enter external calibrationinformation.
 4. The method of claim 3, wherein the displayed value isfurther based on the external calibration information.
 5. The method ofclaim 3, wherein the external calibration information is received froman SMBG or a fingerstick calibration.
 6. The method of claim 3, furthercomprising resetting the known calibration value to a new knowncalibration value, the resetting based at least partially on theexternal calibration information.
 7. The method of claim 3, furthercomprising resetting the seed value to a new seed value, the resettingbased at least partially on the external calibration information.
 8. Themethod of claim 1, further comprising altering the display based on adetermined accuracy of the value.
 9. The method of claim 8, wherein thealtering the displaying includes displaying a range rather than a value,or vice versa.
 10. The method of claim 1, wherein the received seedvalue of a calibration parameter is a user-entered characterization ofdisease state.
 11. The method of claim 10, wherein the user enteredcharacterization of disease state includes an indication of type Idiabetes, type II diabetes, nondiabetic, or prediabetic.
 12. The methodof claim 1, wherein the received seed value of a calibration parameteris a value based on one or more user-entered blood glucose values. 13.The method of claim 1, wherein the displaying of a value correspondingto the received signal includes displaying a graph or table indicatingcurrently measured and historic values of the analyte concentration, andfurther comprising: detecting that a change in calibration has occurred;adjusting one or more calibration parameters of the analyteconcentration sensor according to the change in calibration; andfollowing the adjusting, updating the display of the graph or tableindicating currently measured and historic values of the analyteconcentration according to the adjusted calibration parameters.
 14. Themethod of claim 13, wherein the detecting that a change in calibrationhas occurred includes: detecting a change in a slow-moving average; ordetecting a change in the steady state value.
 15. The method of claim 1,wherein the adjusting is configured to occur at a time when a sensorreading is substantially stable, or within a predetermined range ofreadings for a threshold period of time, whereby an occurrence ofunexpected jumps in readings is reduced.
 16. An analyte concentrationsensor system, comprising: an analyte concentration sensor configured tobe calibrated within a biological system, using a signal from theanalyte concentration sensor, wherein at a steady state, the analyteconcentration value within the biological system is known; a processorcomprising a component of a monitoring device or a device or serveroperatively connected to the monitoring device, wherein the processor isconfigured to: receive a seed value of a calibration parameter; detectwhen an analyte concentration value as measured by the analyteconcentration sensor indwelling in a biological system is at a steadystate; correlate a measurement of the analyte concentration value whenthe biological system is at the detected steady state to the knownanalyte concentration value; thereafter receive a signal from thesensor; and calculate a value corresponding to the received signal, thecalculated value based on the received signal, the known analyteconcentration value, and the seed value; and a display device, whereinthe display device is configured to display the calculated value. 17.The system of claim 16, wherein the received seed value is received froma source including factory calibration information.
 18. The system ofclaim 16, wherein the processor is configured to: detect a behavior inthe received signal outside of a pre-prescribed parameter; and prompt auser to enter external calibration information.
 19. The system of claim18, wherein the processor is configured to reset the known calibrationvalue to a new known calibration value based at least partially on theexternal calibration information.
 20. The system of claim 18, whereinthe processor is configured to reset the seed value to a new seed valuebased at least partially on the external calibration information. 21.The system of claim 16, wherein the processor is configured to alter thedisplay based on a determined accuracy of the value.
 22. The system ofclaim 16, wherein the display device is configured to display a graph ortable indicating currently measured and historic values of the analyteconcentration, and wherein the processor is further configured to:detect that a change in calibration has occurred; adjust one or morecalibration parameters of the analyte concentration sensor according tothe change in calibration; and thereafter update the display of thegraph or table indicating currently measured and historic values of theanalyte concentration according to the adjusted calibration parameters.23. The system of claim 22, wherein the processor is configured todetect that a change in calibration has occurred by detecting a changein a slow-moving average or detecting a change in the steady statevalue.
 24. The system of claim 16, wherein the processor is configuredto adjust one or more calibration parameters at a time when a sensorreading is substantially stable, or within a predetermined range ofreadings for a threshold period of time, whereby an occurrence ofunexpected jumps in readings is reduced.